ORCID Profile
0000-0003-1132-7589
Current Organisation
University of South Australia
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Statistics | Optimisation | Stochastic Analysis And Modelling | Environmental Engineering Modelling | Renewable Power and Energy Systems Engineering (excl. Solar Cells) | Applied Mathematics | Cultural Studies | Consumption And Everyday Life | Environmental Science and Management | Risk Theory | Operations Research | Social And Community Psychology | Numerical and Computational Mathematics not elsewhere classified | Applied Mathematics not elsewhere classified | Environmental Engineering Modelling | Environmental Engineering | Electrical and Electronic Engineering | Photogrammetry And Remote Sensing | Database Management | Atmospheric Aerosols | Aboriginal and Torres Strait Islander Environmental Knowledge | Environmental Management | Applied Statistics | Environmental Education and Extension | Social And Cultural Anthropology
ANZSRC Socio-Economic Objective (SEO)
Climate Change Mitigation Strategies | Atmospheric Processes and Dynamics | Energy systems analysis | Electricity transmission | Wind | Cement and Concrete Materials | Industry | Land and water management | Processed food products and beverages | Mathematical sciences | Climate variability | Rehabilitation of Degraded Urban and Industrial Environments | Urban and Industrial Water Management | Land and water management | Land and water management | Solar-Photovoltaic Energy | Industry costs and structure | Information Processing Services (incl. Data Entry and Capture) | Renewable energy not elsewhere classified (e.g. geothermal) | Expanding Knowledge in the Environmental Sciences | Consumption patterns, population issues and the environment | Expanding Knowledge in the Mathematical Sciences | Electricity, gas and water services and utilities |
Related Links
Publications
Heat stress risk and resilience in the urban environment
Publisher: Elsevier BV
Date: 10-2016
Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets
Publisher: Elsevier BV
Date: 07-2016
Are the Dietary Guidelines for Meat, Fat, Fruit and Vegetable Consumption Appropriate for Environmental Sustainability? A Review of the Literature
Publisher: Apple Academic Press
Date: 12-05-2016
DOI: 10.1201/B20562-20
Changes of waste generation in Australia: Insights from structural decomposition analysis
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.WASMAN.2018.11.004
Abstract: Waste generation is linked to consumption both in households (Final demand) and in the supply chain. Gaining understanding into the driving forces that change of waste generation in the supply chain can contribute to solving issues of waste management. The environmentally-extend input-output model is an effective tool with which to investigate the relationship between economic activities and waste generation. In this paper structural decomposition analysis (SDA) is employed to analyse the determinants of changes of waste generation in Australian economy from 2007-2008 to 2013-2014. Empirical results indicate that the major determinant for the increase of waste generation was change in Final demand's overall level of economic activity. Changes in the production mix of Final demand (mix effect) was responsible for a decrease of waste generation in Australian economy during the period. The Manufacturing sector was found to have the highest waste generation intensity. Meaning that each million $AUD output of the Manufacturing sector resulted in the most amount of waste generation. In addition, technological change has contributed the largest waste generation effect for the Construction sector in 2011-2012. These findings suggest that Final demand, technological changes and sectoral changes are identified as the drivers of Australian waste generation historically. To reduce waste generation, policy must be targeted at altering behaviour of consumption and waste generation, and increasing innovation of new ecological technologies for Australian industry.
Optimisation of Storage for Concentrated Solar Power Plants
Publisher: MDPI AG
Date: 12-12-2014
Modelling of diffuse solar fraction with multiple predictors
Publisher: Elsevier BV
Date: 02-2010
Analyzing the impact of hydrological storage and connected impervious area on the performance of distributed kerbside infiltration systems in an urban catchment
Publisher: Elsevier BV
Date: 05-2022
Multistep Forecasting for Highly Volatile Data using A New Box-Jenkins and GARCH Procedure
Publisher: Academy of Sciences Malaysia
Date: 13-04-2020
DOI: 10.32802/ASMSCJ.2020.SM26(1.14)
Abstract: The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study proposes a new procedure of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance for a highly volatile time series data. The promising results from one-step ahead out-of-s le forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the procedure of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed procedure is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed procedure of multistep ahead forecast enhances the existing procedure of BJ-G which is able to provide a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The procedure adds the value of BJ-G model since it allows the model to describe efficiently the characteristics of the volatile series up to n-step ahead forecast. Keywords: Box-Jenkins, GARCH, highly volatile data, multistep forecast gold price
Artificial Neural Network models for estimating daily solar global UV, PAR and broadband radiant fluxes in an eastern Mediterranean site
Publisher: Elsevier BV
Date: 2015
The economic contribution of seagrass to secondary production in South Australia
Publisher: Elsevier BV
Date: 07-2006
FORECASTING WIND AND SOLAR ENERGY ON SHORT TIME SCALES
Publisher: Cambridge University Press (CUP)
Date: 26-11-2013
Heat resilience in public space and its applications in healthy and low carbon cities
Publisher: Elsevier BV
Date: 2017
Spatial and Activity Preferences During Heat Stress Conditions in Adelaide: Towards Increased Adaptation Capacity of the Built Environment
Publisher: Elsevier BV
Date: 2017
Evaluating tilted plane models for solar radiation using comprehensive testing procedures, at a southern hemisphere location
Publisher: Elsevier BV
Date: 03-2013
Rainfall enhances vegetation growth but does the reverse hold?
Publisher: MDPI AG
Date: 23-07-2014
DOI: 10.3390/W6072127
Optimisation techniques for planning automatic under frequency load shedding in New Zealand's power system
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 26-05-2015
Nonparametric Conditional Heteroscedastic Hourly Probabilistic Forecasting of Solar Radiation
Publisher: MDPI AG
Date: 04-12-2018
DOI: 10.3390/J1010016
Abstract: We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous method catered for the global systematic variation in variance of solar radiation, whereas our new method also caters for the local variation in variance. We test the performance of our new probabilistic forecasting method against our old probabilistic forecasting method at three locations: Adelaide, Darwin, and Mildura. These locations are chosen to represent three distinct climates. The prediction interval coverage probability, prediction interval normalized averaged width and Winkler score results from our new probabilistic forecasting method are encouraging. Our new method performs better than our previous method at Adelaide and Mildura regions with a higher proportion of clear-sky days, whereas our previous method performs better than our new method at Darwin a region with a lower proportion of clear-sky days. These results suggest that the ideal probabilistic forecasting method might be climate specific.
Economic Driven Patterns of the Urban Environment
Publisher: MDPI AG
Date: 16-12-2016
DOI: 10.20944/PREPRINTS201612.0085.V1
Abstract: This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1,263 million in 1991 to 1,409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.
Controlling stored energy in a concentrating solar thermal power plant to maximise revenue
Publisher: Institution of Engineering and Technology (IET)
Date: 05-2015
Forecasting intra-hour variance of photovoltaic power using a new integrated model
Publisher: Elsevier BV
Date: 10-2021
Australian electricity market and price volatility
Publisher: Springer Science and Business Media LLC
Date: 10-12-2016
Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction
Publisher: Elsevier BV
Date: 09-2018
Income Driven Patterns of the Urban Environment
Publisher: MDPI AG
Date: 15-02-2017
DOI: 10.3390/SU9020275
The importance of the stochastic component of climatic variables in simulating the thermal behaviour of domestic dwellings
Publisher: Elsevier BV
Date: 06-1997
Post-processing of solar irradiance forecasts from WRF model at Reunion Island
Publisher: Elsevier BV
Date: 07-2014
A model to detect grazing sensitivity of Myoporum platycarpum in the arid rangelands of South Australia
Publisher: Wiley
Date: 28-06-2006
Heat stress-resistant building design in the Australian context
Publisher: Elsevier BV
Date: 2018
Development of robust meteorological year weather data
Publisher: Elsevier BV
Date: 04-2018
Investigation of the influence of seagrass on fisheries catch level using isotonic regression
Publisher: Springer Science and Business Media LLC
Date: 12-2003
Models of diffuse solar fraction
Publisher: Springer Berlin Heidelberg
Date: 2008
Review of solar irradiance forecasting methods and a proposition for small-scale insular grids
Publisher: Elsevier BV
Date: 11-2013
Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions
Publisher: MDPI AG
Date: 11-07-2017
DOI: 10.3390/EN10070971
Evaluation of the environmental impact of weekly food consumption in different socio-economic households in Australia using environmentally extended input-output analysis
Publisher: Elsevier BV
Date: 03-2015
Short term solar radiation forecasting: Island versus continental sites
Publisher: Elsevier BV
Date: 10-2016
Retrofitting precincts for heatwave resilience: Challenges and barriers in Australian context
Publisher: MDPI AG
Date: 16-01-2015
Correlative coherence modelling of South Australian wind farms
Publisher: Elsevier BV
Date: 04-2013
Estimating industrial solid waste and municipal solid waste data at high resolution using economic accounts: an input–output approach with Australian case study
Publisher: Springer Science and Business Media LLC
Date: 03-2015
Stormwater runoff reduction benefits of distributed curbside infiltration devices in an urban catchment
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.WATRES.2022.118273
Abstract: Distributed infiltration systems can benefit downstream water bodies by reducing the runoff flowrate and volume discharges from the catchment. Investigating their runoff flowrate and volume reduction potential at the catchment scale will inform decision makers regarding their efficacy for managing catchment outflows. To this end, we conducted field investigations at the residential catchment scale for three years. The study monitored the catchment for one year before the installation of leaky well systems (preinstallation) and two years after installation (postinstallation). The hydrological model, calibrated to preinstallation catchment outflows, acted as a virtual control tool. Runoff flow outputs from the control model and two years of monitored runoff flow data from the postinstallation period were analysed using statistical methods. The statistical tests showed a significant 13% reduction in average flowrates in storms with a corresponding runoff flowrate of up to 50 L/s. The study further reported the ability of infiltration systems to reduce runoff volume in the catchment by 9%. This reduction was not significant, however, as per the results of the statistical analysis. We then fitted the generalized linear model (GLM) to the monitored and simulated runoff volume data. This enabled us to break down the effect of curbside infiltration systems on runoff volume according to corresponding peak flowrates during the storm. The results of the two-way ANOVA performed to detect significant differences in the regression slopes of the GLM indicated that curbside infiltration systems significantly reduced runoff volume for storms when the runoff flowrates remained below 100 L/s.
Does the Australian Nationwide House Energy Rating Scheme ensure heat resistance?
Publisher: Unpublished
Date: 2016
Towards an extended framework for the general dynamic theory of biogeography
Publisher: Wiley
Date: 03-10-2020
DOI: 10.1111/JBI.13944
Volatility of wind energy using high frequency data
Publisher: ACTAPRESS
Date: 2010
Effects of Australian Economic Activities on Waste Generation and Treatment
Publisher: MDPI AG
Date: 31-07-2017
Characterising Seasonality of Solar Radiation and Solar Farm Output
Publisher: MDPI AG
Date: 18-01-2020
DOI: 10.3390/EN13020471
Abstract: With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the ision of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one ides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting.
Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market
Publisher: Elsevier BV
Date: 08-2021
Assessing one-minute diffuse fraction models based on worldwide climate features
Publisher: Elsevier BV
Date: 11-2021
How to cope with heat waves in the home
Publisher: Wiley
Date: 31-10-2014
Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review
Publisher: MDPI AG
Date: 05-01-2022
DOI: 10.3390/EN15010370
Abstract: Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For ex le, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output.
Dieback of Stringybark Forests in The Mount Lofty Ranges - phase 1
Publisher: University of South Australia & University of Adelaide
Date: 2022
DOI: 10.25954/FR78-QH71
Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales
Publisher: MDPI AG
Date: 02-05-2018
DOI: 10.3390/EN11051119
Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models
Publisher: Elsevier BV
Date: 04-2019
Time-series analysis of climatic variables
Publisher: Elsevier BV
Date: 11-1995
Editorial to the Special Issue ISES Solar World Congress 2013 (SWC 2013) “Renewables working together for all”
Publisher: Elsevier BV
Date: 11-2015
Comparing diffuse radiation models with one predictor for partitioning incident PAR radiation into its diffuse component in the eastern Mediterranean basin
Publisher: Elsevier BV
Date: 08-2010
Principles and Key Applications: Principles and Applications of Synthetic Solar Irradiance
Publisher: AIP Publishing LLCMelville, New York
Date: 24-04-2021
DOI: 10.1063/9780735421820_002
Abstract: In this chapter we first describe the basic concepts of synthetic generation of time series data. We examine several of the realms where it is needed. One area is downscaling from a coarse temporal resolution to a higher frequency. For ex le, when estimating the performance of solar cells, one-minute data is more useful than hourly, but it may not be available. Also, infilling missing data is crucial if one is forecasting output from a solar farm. On the other hand, generating any number of years of data from a limited number of measured years—known as bootstrapping—is useful for estimating risk for farms and other solar installations. In energy markets, there is scope for generating possible future trajectories for either solar radiation time series or financial instruments that depend on them. In describing these situations and others, we briefly explain the mechanisms for these computations. We end by describing in detail some specific ex le applications of synthetic solar irradiance tools.
Generating Synthetic Rainfall Total Using Multivariate Skew-t and Checkerboard Copula of Maximum Entropy
Publisher: Springer Science and Business Media LLC
Date: 03-2017
Generating Synthetic Rainfall on Various Timescales—Daily, Monthly and Yearly
Publisher: Springer Science and Business Media LLC
Date: 02-07-2009
Simulation-optimisation of a granularity controlled consumer supply network using genetic algorithms
Publisher: ASTES Journal
Date: 2018
DOI: 10.25046/AJ030654
Passive activity observation (PAO) method to estimate outdoor thermal adaptation in public space: case studies in Australian cities
Publisher: Springer Science and Business Media LLC
Date: 18-06-2020
DOI: 10.1007/S00484-018-1570-Y
Abstract: Outdoor thermal comfort is influenced by people's climate expectations, perceptions and adaptation capacity. Varied in idual response to comfortable or stressful thermal environments results in a deviation between actual outdoor thermal activity choices and those predicted by thermal comfort indices. This paper presents a passive activity observation (PAO) method for estimating contextual limits of outdoor thermal adaptation. The PAO method determines which thermal environment result in statistically meaningful changes may occur in outdoor activity patterns, and it estimates thresholds of outdoor thermal neutrality and limits of thermal adaptation in public space based on activity observation and microclimate field measurement. Applications of the PAO method have been demonstrated in Adelaide, Melbourne and Sydney, where outdoor activities were analysed against outdoor thermal comfort indices between 2013 and 2014. Adjusted apparent temperature (aAT), adaptive predicted mean vote (aPMV), outdoor standard effective temperature (OUT_SET), physiological equivalent temperature (PET) and universal thermal comfort index (UTCI) are calculated from the PAO data. Using the PAO method, the high threshold of outdoor thermal neutrality was observed between 24 °C for optional activities and 34 °C for necessary activities (UTCI scale). Meanwhile, the ultimate limit of thermal adaptation in uncontrolled public spaces is estimated to be between 28 °C for social activities and 48 °C for necessary activities. Normalised results indicate that city-wide high thresholds for outdoor thermal neutrality vary from 25 °C in Melbourne to 26 °C in Sydney and 30 °C in Adelaide. The PAO method is a relatively fast and localised method for measuring limits of outdoor thermal adaptation and effectively informs urban design and policy making in the context of climate change.
The role of residential tree arrangement: A scoping review of energy efficiency in temperate to subtropical climate zones
Publisher: Elsevier BV
Date: 09-2202
The Mekong-applications of value at risk (VaR) and conditional value at risk (CVaR) simulation to the benefits, costs and consequences of water resources development in a large river basin
Publisher: Elsevier BV
Date: 02-2007
Optimisation of photovoltaic system and storage
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 10-05-2017
Limits of thermal adaptation in cities: outdoor heat-activity dynamics in Sydney, Melbourne and Adelaide
Publisher: Informa UK Limited
Date: 04-07-2018
Truncated Gaussian Hidden Markov models and wind speed spatio-temporal series
Publisher: IEEE
Date: 06-2018
Erratum: Time series analysis of climatic variables (Solar Energy (1995) 55:5 (377-388))
Publisher: Elsevier BV
Date: 06-1996
Generation of synthetic sequences of half-hourly temperature
Publisher: Wiley
Date: 12-2008
DOI: 10.1002/ENV.905
Solar Energy Resource Assessment in Chile: Satellite Estimation and Ground Station Measurement
Publisher: Elsevier BV
Date: 2014
Impact of climate change on the design of energy efficient residential building envelopes
Publisher: Elsevier BV
Date: 2015
Generation of synthetic sequences of electricity demand: Application in South Australia
Publisher: Elsevier BV
Date: 11-2007
A decision-making tool for determination of storage capacity in grid-connected PV systems
Publisher: Elsevier BV
Date: 12-2018
Verification of deterministic solar forecasts
Publisher: Elsevier BV
Date: 11-2020
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
Publisher: AIP Publishing LLC
Date: 2015
DOI: 10.1063/1.4907458
Evaluation of the diffusive gradients in thin-films (DGT) technique for measuring nitrate and ammonium in soil
Publisher: CSIRO Publishing
Date: 20-02-2023
DOI: 10.1071/EN22107
Abstract: Environmental context Nitrate (NO3−) and ammonium (NH4+) are the most important soil nitrogen forms for plant growth. However, conventional extraction techniques may introduce artefacts affecting the measurement of plant-available N concentrations following s ling and s le preparation processes. This is the first study of the DGT technique being used to measure NO3-N and NH4-N in a wide range of soils, compared with conventional KCl extraction, and examined different factors that contribute to the plant-availability of these ions in soils. The knowledge would help to optimise soil nitrogen management practices, increase economic benefits and reduce environmental impacts. Rationale The availability of soil nitrogen for plant uptake can be affected by numerous soil factors such as soil texture, moisture and organic matter content, temperature and microbial activity. Conventional extraction techniques may affect the measurement of plant-available N concentrations following s ling and s le preparation processes, including drying, sieving, homogenising, freezing and thawing. The diffusive gradients in thin-films (DGT) technique can overcome some limitations of the conventional extraction techniques and has been used to successfully estimate the plant-available fractions of nutrients, such as P, K, Zn, Cu and Mn in soils. Therefore, it is important to evaluate the use of DGT for measuring NO3− and NH4+ in a wide variety of soils and examine the factors that contribute to the plant-availability of these ions in soils. Methodology The experiment evaluated the ability of the DGT technique to measure NO3-N and NH4-N in soils using binding layers containing A520E anion exchange resin or Microlite® PrCH cation exchange resin, respectively. The DGT results were compared to those from conventional KCl extraction. Results The A520E- and PrCH-DGTs showed good detection limits for NO3-N (6.90 µg L−1) and NH4-N (6.23 µg L−1) and were able to measure potentially available NO3-N and NH4-N in unfertilised soils. The mass of NO3-N and NH4-N that accumulated on the DGT device increased linearly across soil concentrations ranging from 5 to 300 mg kg−1 NO3-N (depending on soil type) and 5–300 mg kg−1 NH4-N which is equivalent to fertiliser rates of 75–450 kg ha−1 N. DGTs were used to measure potentially available NO3-N and NH4-N in ten soils with various physical and chemical properties. The DGT results were compared with conventional KCl extraction used to determine soil mineral N. DGT and KCl extraction measured values were significantly correlated with each other for NO3-N (R2 = 0.53 P-value 0.001), but the relationship between the two measurements was weaker for NH4-N (R2 = 0.20, P-value = 0.045). Discussion The results suggest that the two methods s le different N pools in the soils, with DGT targeting the NO3-N and NH4-N that are available in soil pore water and attached to labile solid phases.
Maximising revenue via optimal control of a concentrating solar thermal power plant with limited storage capacity
Publisher: Institution of Engineering and Technology (IET)
Date: 05-2016
Assessing net energy consumption of Australian economy from 2004–05 to 2014–15: Environmentally-extended input-output analysis, structural decomposition analysis, and linkage analysis
Publisher: Elsevier BV
Date: 04-2019
Spatial-temporal forecasting of solar radiation
Publisher: Elsevier BV
Date: 03-2015
Estimating the potential for solar energy utilization in Chile by satellite-derived data and ground station measurements
Publisher: Elsevier BV
Date: 11-2015
Assessment of solid waste generation and treatment in the Australian economic system: A Closed Waste Supply-Use model
Publisher: Elsevier BV
Date: 08-2018
On the use of BRL model for daily and hourly solar radiation components assessment in a semiarid climate
Publisher: Springer Science and Business Media LLC
Date: 02-2020
School Students participation in monitoring solar radiation components: Preliminary results for UVB and UVA solar radiant fluxes
Publisher: Elsevier BV
Date: 03-2012
A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study
Publisher: ASME International
Date: 18-08-2020
DOI: 10.1115/1.4047852
Abstract: This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42% for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.
Can the Excess Heat Factor Indicate Heatwave-Related Morbidity? A Case Study in Adelaide, South Australia
Publisher: Springer Science and Business Media LLC
Date: 22-12-2015
DOI: 10.1007/S10393-015-1085-5
Abstract: Although heatwave-related excess mortality and morbidity have been widely studied, results are not comparable spatially and often longitudinally because of different heatwave definitions applied. The excess heat factor (EHF) quantifies heatwave intensity relative to the local climate, enabling cross-regional comparisons. Previous studies have shown a strong relationship between EHFs and daily mortality during severe heatwaves. An extensive study about the relationship between EHFs and daily morbidity compared to the currently applied heatwave thresholds in Adelaide has not yet been undertaken. This paper analyzes the association of EHFs with daily morbidity between 2008 and 2014 in the Adelaide metropolitan region, South Australia, and probes three different approaches to calculate the EHF. The EHF is found to differentiate days with heatwave-related excess morbidity significantly better than other widely used weather parameters, resulting in fewer days per year with heatwave alerts than using previously proposed methods. The volume of excess morbidity can be predicted by the EHF more reliably with a model proposed for the SA Ambulance Service to support their heatwave preparation plan.
The viability of electrical energy storage for low-energy households
Publisher: Elsevier BV
Date: 10-2017
Validation of the Predictive Capabilities of the Sbrc-G in Vitro Assay for Estimating Arsenic Relative Bioavailability in Contaminated Soils
Publisher: American Chemical Society (ACS)
Date: 23-10-2014
DOI: 10.1021/ES503695G
Abstract: A number of bioaccessibility methodologies have the potential to act as surrogate measures of arsenic (As) relative bioavailability (RBA), however, validation of the in vivo-in vitro relationship is yet to be established. Validation is important for human health risk assessment in order to ensure robust models for predicting As RBA for refining exposure via incidental soil ingestion. In this study, 13 As-contaminated soils were assessed for As RBA (in vivo swine model) and As bioaccessibility (Solubility Bioaccessibility Research Consortium gastric phase extraction SBRC-G). In vivo and in vitro data were used to assess the validity of the As RBA-bioaccessibility correlation previously described by Juhasz et al. (2009). Arsenic RBA and bioaccessibility in the 13 soils ranged from 6.8±2.4% to 70.5±6.8% and 5.7±0.3% to 78.4±0.8% respectively with a strong linear relationship (R2=0.75) between in vivo and in vitro assays. When the As in vivo-in vitro correlation was compared that of Juhasz et al. (2009), there was no significant difference (P>0.05) indicating that the relationship between As RBA and As bioaccessibility was consistent thereby demonstrating its validation. For these data, a grouped linear regression model was developed (R2=0.82) with a slope and y-intercept of 0.84 and 3.56 respectively. A number of cross validation methodologies (2-fold, repeat random subs ling, leave one out) were utilized to determine the performance of the linear regression model. Residuals and prediction errors ranged from 5.4 to 9.4 and 6.9-12.2 respectively illustrating the capacity of the SBRC-G to accurately predict As RBA in contaminated soil.
Evaluating the Performance of a Hydrological Model to Represent Curbside Distributed Infiltration Wells in a Residential Catchment
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2021
Post-processing of Solar Irradiance Forecasts from WRF Model at Reunion Island
Publisher: Elsevier BV
Date: 2014
Rainfall modelling using the sum of independent gamma variables
Publisher: AIP
Date: 2012
DOI: 10.1063/1.4757496
Dust Event Impact on Photovoltaic Systems : Role of humidity in soiling and self-cleaning
Publisher: IEEE
Date: 08-2018
Quasi-Latin designs
Publisher: Institute of Mathematical Statistics
Date: 2012
DOI: 10.1214/12-EJS732
Potential residential tree arrangement to optimise dwelling energy efficiency
Publisher: Elsevier BV
Date: 04-2022
Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach
Publisher: MDPI AG
Date: 21-09-2021
DOI: 10.3390/EN14186005
Abstract: Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance.
Habitat diversity, resource availability and island age in the species‐area relationship
Publisher: Wiley
Date: 20-02-2023
DOI: 10.1111/JBI.14571
Abstract: The island species–area relationship (ISAR) assumes that the area of islands is homogeneous, or scales with environmental heterogeneity across an archipelago, which is not always the case. We compare the performance of models that adjust or substitute for island area with measures of habitat ersity, island age and resource availability for two taxonomic groups. Five hotspot archipelagos (Azores, Galapagos, Hawaii, Cape Verde, Canary Islands). Vascular plants, birds. We used the mathematical framework of the power law to compare relevant models, treating the one containing only area as a null model against which others were compared. Data were collated from databases and the literature. Models were compared using linear regression within archipelagos and via mixed effect models with archipelago as a random effect. Weighting of island area by habitat ersity and resource availability systematically improved statistical significance and model fits versus the area only power law. Models including island age did not show the same systematic improvement in model fits. For vascular plants, weighting islands by resource availability (energy and water) performed better than weighting by habitat ersity, although for birds these weightings performed equally well. Given that islands within archipelagos are fairly heterogeneous in climate, topography and geology, it is worth accounting for this in ISARs. Our results suggest that, for islands in volcanic hotspot archipelagos, this is best done by using direct measures of habitat ersity and resource availability rather than using island age as a proxy. We, therefore, recommend using direct measures, rather than proxies, when investigating the drivers of bio ersity patterns on islands.
Policy recommendations to increase urban heat stress resilience
Publisher: Elsevier BV
Date: 09-2018
Compiling and using input-output frameworks through collaborative virtual laboratories
Publisher: Elsevier BV
Date: 07-2014
DOI: 10.1016/J.SCITOTENV.2014.03.062
Abstract: Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models. The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions. The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users. Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties.
The level of complexity needed for weather data in models of solar system performance
Publisher: Elsevier BV
Date: 2001
Decomposing global solar radiation into its direct and diffuse components
Publisher: Elsevier BV
Date: 12-2013
Achieving waiting list reform: A pilot program integrating waiting time, category and patient factors
Publisher: CSIRO Publishing
Date: 2012
DOI: 10.1071/AH11997
Abstract: Equity in resource allocation is central to the tenet of social justice in health care. The management of surgical waiting lists is of critical importance to clinicians, patients and regulators. In most hospital environments, the basic process has remained unchanged for decades. Patients are assigned to one of three urgency-related categories. Clinicians consequently administer three competing patient pools. The basis by which patients are selected for treatment may be difficult to define. The specific clinical circumstances of each patient are often unreported and may be unknown to those administering the list. Waiting list bias is also recognised. This may reflect clinician advocacy, pressure to meet category timeframe restrictions or perceived training requirements. In this environment, it is difficult to demonstrate propriety in care. We report the implementation of a pilot program to redesign waiting list management within a South Australian public hospital unit. This allows assemblage of patients into a single list. Overall priority is determined by balancing clinical acuity and waiting time. The determination of acuity takes into account both the primary category and the specific characteristics of each patient that are relevant to their intended procedure. Uniquely, the process is applicable to lists containing patients with dissimilar conditions. This paper reviews the limitations of current approaches in meeting reasonable community expectations. The principles and social justification underpinning this reform are introduced. Finally, the benefits offered by the program are discussed and interim results are reported. What is known about this topic? Current models for the management of hospital waiting lists have remained largely unchanged for several decades. Typically patients are allocated to urgent, semi-urgent and non-urgent categories of care. No methodology exists to systematically integrate these groups, or to account for specific patient factors. In this void, propriety in management is difficult to establish or defend. What does this paper add? A program is reported that unifies all categories of patients into a single prioritised waiting list. The order of patients is dynamic, and transparently reflects waiting time, category assignment and relevant in idual patient factors. Uniquely, the program is applicable to lists containing patients with erse clinical conditions. What are the implications for clinicians? Adoption of new technology is essential if reasonable community expectations in waiting list management are to be met. The current program provides unambiguous, defensible prioritisation of all patients awaiting care. The present reliance on in idual managers is reduced, and the unique circumstances of each patient are recognised. We believe this approach affords significant benefit to patients, practitioners and regulators.
Bayesian statistical analysis applied to solar radiation modelling
Publisher: Elsevier BV
Date: 2013
Information Disclosure in Mobile Device: Examining the Influence of Information Relevance and Recipient
Publisher: Hawaii International Conference on System Sciences
Date: 2021
Understanding South Australian rainfall trends and step changes
Publisher: Wiley
Date: 14-03-2015
DOI: 10.1002/JOC.3982
Time series modelling of solar radiation
Publisher: Springer Berlin Heidelberg
Date: 2008
Models of diffuse solar radiation
Publisher: Elsevier BV
Date: 04-2008
Assessment of Heatwave Impacts
Publisher: Elsevier BV
Date: 2016
Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions
Publisher: Elsevier BV
Date: 06-2019
Are the Dietary Guidelines for Meat, Fat, Fruit and Vegetable Consumption Appropriate for Environmental Sustainability? A Review of the Literature
Publisher: Apple Academic Press
Date: 12-05-2016
DOI: 10.1201/B20562-20
Are the dietary guidelines for meat, fat, fruit and vegetable consumption appropriate for environmental sustainability? A review of the literature
Publisher: MDPI AG
Date: 12-06-2014
DOI: 10.3390/NU6062251
Climate change adaptation and carbon emissions in green urban spaces: case study of Adelaide
Publisher: Elsevier BV
Date: 05-2020
A proposed long-term thermal comfort scale
Publisher: Informa UK Limited
Date: 30-10-2020
Nonparametric short-term probabilistic forecasting for solar radiation
Publisher: Elsevier BV
Date: 08-2016
Satellite-augmented diffuse solar radiation separation models
Publisher: AIP Publishing
Date: 03-2019
DOI: 10.1063/1.5087463
Abstract: Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting) their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.
Simulation-optimisation of multi-product, multi- period consumer supply network using genetic algorithms
Publisher: IEEE
Date: 09-2017
Time series model for real-time forecasting of Australian photovoltaic solar farms power output
Publisher: AIP Publishing
Date: 07-2021
DOI: 10.1063/5.0050621
Abstract: Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP) model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms.
Estimating informal household food waste in developed countries: The case of Australia
Publisher: SAGE Publications
Date: 23-09-2014
Abstract: Food waste is a global problem. In Australia alone, it is estimated that households throw away AU$5.2 billion worth of food (AU$616 per household) each year. Developed countries have formal waste management systems that provide measures of food waste. However, much remains unknown about informal food waste disposal routes and volumes outside of the formal system. This article provides indicative metrics of informal food waste by identifying, in detail, five of the dominant informal food waste disposal routes used by Australian households: home composting, feeding scraps to pets, sewer disposal, giving to charity, and dumping or incineration. Informal waste generation rates are then calculated from three primary data sources, in addition to data from previous Australian and UK surveys, using a weighted average method in conjunction with a Monte-Carlo simulation. We find that the average Australian household disposes of 2.6 kgs of food waste per week through informal routes (1.7 kgs via household composting, 0.2 kgs via animals, and 0.6 kgs via sewage). This represents 20% of Australian household food waste flows. Our results highlight that informal food waste is a sizable food waste flow from Australian homes, deserving of greater research and government attention. Our examination of the full extent of food waste by disposal mode provides waste managers and policy makers with clear disposal routes to target for behaviour change and positive environmental outcomes.
Integrating climate change into meteorological weather data for building energy simulation
Publisher: Elsevier BV
Date: 2019
Characterizing the Stormwater Runoff Quality and Evaluating the Performance of Curbside Infiltration Systems to Improve Stormwater Quality of an Urban Catchment
Publisher: MDPI AG
Date: 22-12-2021
DOI: 10.3390/W14010014
Abstract: The conveyance of stormwater has become a major concern for urban planners, considering its harmful effects for receiving water bodies, potentially disturbing their ecosystem. Therefore, it is important to characterize the quality of catchment outflows. This information can assist in planning for appropriate mitigation measures to reduce stormwater runoff discharge from the catchment. To achieve this aim, the article reports the field data from a typical urban catchment in Australia. The pollutant concentration from laboratory testing is then compared against national and international reported values. In addition, a stochastic catchment model was prepared using MUSIC. The study in particular reported on the techniques to model distributed curbside leaky wells with appropriate level of aggregation. The model informed regarding the efficacy of distributed curbside leaky well systems to improve the stormwater quality. The results indicated that catchment generated pollutant load, which is typical of Australian residential catchments. The use of distributed storages only marginally improves the quality of catchment outflows. It is because ability of distributed leaky wells depended on the intercepted runoff volume which is dependent on the hydrological storage volume of each device. Therefore, limited storage volume of current systems resulted in higher contributing area to storage ratio. This manifested in marginal intercepted volume, thereby only minimum reduction in pollutant transport from the catchment to outlet. Considering strong correlation between contributing impervious area and runoff pollutant generation, the study raised the concern that in lieu of following the policy of infill development, there can be potential increase in pollutant concentration in runoff outflows from Australian residential catchments. It is recommended to monitor stormwater quality from more residential catchments in their present conditions. This will assist in informed decision-making regarding adopting mitigations measures before considering developments.
Probabilistic Forecasting of Wind and Solar Farm Output
Publisher: MDPI AG
Date: 20-08-2021
DOI: 10.3390/EN14165154
Abstract: Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting.
Policy insights from a green supply chain optimisation model
Publisher: Informa UK Limited
Date: 17-09-2015
Generating synthetic five-minute solar irradiance values from hourly observations
Publisher: Elsevier BV
Date: 05-2017
Corrigendum to Spatial-temporal forecasting of solar radiation [Renew Energy 75 (2015) 607-616]
Publisher: Elsevier BV
Date: 09-2015
Resilience to heat in public space: a case study of Adelaide, South Australia
Publisher: Informa UK Limited
Date: 02-12-2016
Drivers and barriers to population heatwave resilience in South Australia
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.SCITOTENV.2016.07.028
Abstract: Heatwaves are the most dangerous natural hazard to health in Australia. The frequency and intensity of heatwaves will increase due to climate change and urban heat island effects in cities, aggravating the negative impacts of heatwaves. Two approaches exist to develop population heat stress resilience. Firstly, the most vulnerable social groups can be identified and public health services can prepare for the increased morbidity. Secondly, the population level of adaptation and the heat stress resistance of the built environment can be increased. The evaluation of these measures and their efficiencies has been fragmented across research disciplines. This study explored the relationships between the elements of heat stress resilience and their potential demographic and housing drivers and barriers. The responses of a representative online survey (N=393) about heat stress resilience at home and work from Adelaide, South Australia were analysed. The empirical findings demonstrate that heat stress resistant buildings increased adaptation capacity and decreased the number of health problems. Air-conditioning increased dependence upon it, limited passive adaptation and only people living in homes with whole-house air-conditioning had less health problems during heatwaves. Tenants and respondents with pre-existing health conditions were the most vulnerable, particularly as those with health conditions were not aware of their vulnerability. The introduction of an Energy Performance Certificate is proposed and discussed as an effective incentive to increase the heat stress resistance of and the general knowledge about the built environment.
Generalized Logarithmic Species-Area Relationship Resolves the Arrhenius-Gleason Debate
Publisher: Springer Science and Business Media LLC
Date: 22-02-2023
DOI: 10.1007/S10666-023-09873-6
Abstract: The species-area relationship (SAR) is widely applied in ecology. Mathematically, it is usually expressed as either a semi-log or power-law relationship, with the former being introduced by Gleason and the latter by Arrhenius. We here resolve the dispute about which form of the SAR to prefer by introducing a novel model that smoothly transforms between the Gleason semi-log (GSL) and Arrhenius power law (APL) forms. The model introduced has the form of ln q ( S ) = a + z ln A , with ln q being a generalized logarithmic function, which is a linear map ( y = x ) for q = 0 and a logarithmic map ( y = ln x ) for q = 1 and q can take any intermediate value between 0 and 1. We applied this model to 100 datasets (mostly islands), linking species richness to island area. The APL was the preferred model in 68% of head-to-head comparisons with the GSL. Both models were supported in 40% of cases. In just under half (44%) of the cases, an intermediate model best explained the data. The results demonstrate the utility of a simple intermediate SAR model. Visualizing the profile of the range of model fits for all q ∈ [0 , 1] (a q chart) allows us to gain extra insight into SARs not yielded by head-to-head comparisons of GSL and APL. The mathematics related to the generalized logarithmic function introduced here should have applications to other areas of ecological modelling.
Assessment of solar radiation components in Brazil using the BRL model
Publisher: Elsevier BV
Date: 08-2017
Modelling and simulation of volumetric rainfall for a catchment in the murray-darling basin
Publisher: Cambridge University Press (CUP)
Date: 08-09-2016
DOI: 10.1017/S1446181116000183
Abstract: We discuss modelling and simulation of volumetric rainfall in a catchment of the Murray–Darling Basin – an important food production region in Australia that was seriously affected by a recent prolonged drought. Consequently, there has been sustained interest in development of improved water management policies. In order to model accumulated volumetric catchment rainfall over a fixed time period, it is necessary to sum weighted rainfall depths at representative sites within each sub-catchment. Since sub-catchment rainfall may be highly correlated, the use of a Gamma distribution to model rainfall at each site means that catchment rainfall is expressed as a sum of correlated Gamma random variables. We compare four different models and conclude that a joint probability distribution for catchment rainfall constructed by using a copula of maximum entropy is the most effective.
Developing age-size relationships for long lived tree species
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2014
DOI: 10.1142/S0218339014400087
Abstract: Calculating the age of trees is often desirable in vegetation studies, but is sometimes difficult. In arid areas in particular, tree rings may not be annual, and growth may be related more to rainfall than annual cycles. A relationship between age and trunk circumference was developed for two species, Acacia aneura and Myoporum platycarpum, based on measurements of trees of known age ( years) growing on permanent quadrats on the Koonamore Reserve, in semi-arid South Australia. Extrapolation beyond the known ages was made by finding the maximum girth of mature trees in a larger population and using this to estimate an asymptote to which the curve is constrained to approach. We envisage that the techniques developed here could be applied to other species of a similar nature, those for which there is no relationship between number of tree rings and age.
Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model
Publisher: Elsevier BV
Date: 2013
Modelling catchment rainfall using sum of correlated gamma variables
Publisher: Penerbit UTM Press
Date: 15-06-2013
DOI: 10.11113/JT.V63.1918
Abstract: One of the major difficulties in simulating rainfall is the need to accurately represent rainfall accumulations. An accurate simulation of monthly rainfall should also provide an accurate simulation of yearly rainfall by summing the monthly totals. A major problem in this regard is that rainfall distributions for successive months may not be independent. Thus the rainfall accumulation problem must be represented as the summation of dependent random variables. This study is aimed to show if the statistical parameters for several stations within a particular catchment is known, then a weighted sum is used to determine a rainfall model for the entire local catchment. A spatial analysis for the sum of rainfall volumes from four selected meteorological stations within the same region using the monthly rainfall data is conducted. The sum of n correlated gamma variables is used to model the sum of monthly rainfall totals from four stations when there is significant correlation between the stations.
Influence of adhesive drapes on intraperitoneal volume and pressure during laparoscopy
Publisher: Wiley
Date: 08-2002
DOI: 10.1046/J.1445-2197.2002.02481.X
Abstract: The influence of adhesive skin drapes on abdominal wall compliance during laparoscopy has not previously been studied. The effect of removing an adhesive abdominal drape on intraperitoneal volume and pressure was studied in 15 patients undergoing a variety of laparoscopic procedures. The internal consistency of this data was evaluated by comparing the observed response to that which was predicted from analysis based on the theory of elasticity. Removal of an adhesive skin drape after induction of a 15-mmHg pneumoperitoneum was associated with changes in intraperitoneal pressure and volume. These changes were statistically significant, highly predictable and clinically relevant. On the basis of the present observations, we recommend that extensive coverage by adhesive drapes should be avoided for those patients or procedures in which elevated intraperitoneal pressure may be particularly deleterious.
A novel ensemble learning approach for hourly global solar radiation forecasting
Publisher: Springer Science and Business Media LLC
Date: 06-10-2021
Estimation of the thermal sensitivity of a dwelling to variations in architectural and behavioural parameters
Publisher: Springer Science and Business Media LLC
Date: 2003
Analysis of Wind Farm Output: Estimation of Volatility Using High-Frequency Data
Publisher: Springer Science and Business Media LLC
Date: 02-03-2013
Relationship between Pb relative bioavailability and bioaccessibility in phosphate amended soil: Uncertainty associated with predicting Pb immobilization efficacy using in vitro assays
Publisher: Elsevier BV
Date: 10-2019
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
Publisher: AIP Publishing LLC
Date: 2015
DOI: 10.1063/1.4907458
Quantification of indirect waste generation and treatment arising from Australian household consumption: A waste input-output analysis
Publisher: Elsevier BV
Date: 06-2020
Erosion and Sediment Transport Modeling: A Systematic Review
Publisher: MDPI AG
Date: 12-07-2023
DOI: 10.3390/LAND12071396
Abstract: Soil erosion and sediment transport have significant consequences, including decreased agricultural production, water quality degradation, and modification to stream channels. Understanding these processes and their interactions with contributing factors is crucial for assessing the environmental impacts of erosion. The primary objective of this review is to identify a suitable soil erosion and sediment transport model for catchment-scale application. The study considers various model selection processes, including model capability and the spatial and temporal domains for assessing spatiotemporal distributions. The review acknowledges the limitations, uncertainties, and unrealistic assumptions associated with soil erosion and sediment transport models. Models are usually developed with a particular objective, which demands an assessment of capabilities, spatial, and temporal applicability, and catchment-scale applicability. Distributed models are often preferred for catchment-scale applications, as they can adequately account for spatial variations in erosion potential and sediment yield, aiding in the evaluation of erosion-contributing elements and planning erosion control measures. Based on the findings of this study, the authors encourage utilizing models (such as Soil and Water Assessment Tool (SWAT) or Automated Geospatial Watershed Assessment Tool (AGWA)) that can forecast net erosion as a function of sediment output for catchment erosion and sediment yield modeling. This review helps researchers and practitioners involved in erosion and sediment modeling by guiding the selection of an appropriate model type based on specific modeling purposes and basin scale. By choosing appropriate models, the accuracy and effectiveness of sediment yield estimation and erosion control measures can be improved.
The analytic solution of the differential equations describing heat flow in houses
Publisher: Elsevier BV
Date: 11-2002
Estimating effective dust particle size from satellite observations
Publisher: Elsevier BV
Date: 08-2018
Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study
Publisher: Elsevier BV
Date: 04-2019
Generation of synthetic solar datasets for risk analysis
Publisher: Elsevier BV
Date: 07-2019
Worldwide Climate Modeling of Diffuse Solar Fraction from One-minute Irradiance
Publisher: International Solar Energy Society
Date: 2019
Impacts of urban form and urban heat island on the outdoor thermal comfort: a pilot study on Mashhad
Publisher: Springer Science and Business Media LLC
Date: 18-02-2021
Generating synthetic sequences of global horizontal irradiation
Publisher: Elsevier BV
Date: 03-2018
Predicting Arsenic Relative Bioavailability Using Multiple in Vitro Assays: Validation of in Vivo–in Vitro Correlations
Publisher: American Chemical Society (ACS)
Date: 28-08-2015
Abstract: In this study, previously established arsenic (As) in vivo-in vitro correlations (IVIVC) were assessed for their validity using an independent data set comprising As relative bioavailability (RBA) and bioaccessibility values for 13 herbicide- and mine-impacted soils. The validation process established the correlation between As RBA (swine model) and bioaccessibility (five in vitro assays), determined whether correlations differed significantly from previous relationships and assessed model bias and error. The capacity of in vitro assays to predict As RBA was demonstrated by the strength of IVIVC goodness of fit ranged from 0.53 (DIN-I) to 0.74 (UBM-I). When compared to previous IVIVC (Juhasz et al. Environ. Sci. Technol. 2009 , 43 , 9487 Juhasz et al. J. Hazard. Mater. 2011 , 197 , 161 ), there was no significant difference (P < 0.01) in the slope and y-intercept for IVG-G, UBM-G, and UBM-I indicating the consistency of these assays for predicting As RBA. However, variability in model bias and prediction error was observed with significantly lower (P < 0.01) error determined for IVG-G suggesting that As RBA predictions using IVG-G may be more robust compared to UBM-G and UBM-I. In contrast, differences in the slope and/or y-intercept were observed for SBRC-I, IVG-I, PBET-G, PBET-I, DIN-G, and DIN-I suggesting that these methodologies may not be suitable for predicting As RBA.
Generation of synthetic sequences of electricity demand with applications
Publisher: Springer US
Date: 2010
Thermal performance of solar air heaters — Experimental correlation
Publisher: Elsevier BV
Date: 05-1996
Rescuing Food from the Organics Waste Stream to Feed the Food Insecure: An Economic and Environmental Assessment of Australian Food Rescue Operations Using Environmentally Extended Waste Input-Output
Publisher: MDPI AG
Date: 21-04-2015
DOI: 10.3390/SU7044707
Landsat surface temperature data analysis for urban heat resilience: Case study of Adelaide
Publisher: Springer International Publishing
Date: 2017
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