ORCID Profile
0000-0002-2290-6749
Current Organisation
University of Southern Queensland
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Cardiorespiratory Medicine and Haematology | Cardiology (incl. Cardiovascular Diseases)
Publisher: MDPI AG
Date: 08-03-2018
DOI: 10.3390/EN11030596
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 09-2019
Publisher: MDPI AG
Date: 04-10-2017
DOI: 10.3390/F8100379
Publisher: Elsevier BV
Date: 12-2019
Publisher: Informa UK Limited
Date: 06-12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 05-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2017
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 02-2019
Publisher: Elsevier BV
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 11-2018
Publisher: Informa UK Limited
Date: 04-07-2019
Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.SCITOTENV.2019.134230
Abstract: A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994-2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 03-01-2018
Publisher: MDPI AG
Date: 25-05-2022
Abstract: Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.
Publisher: AIP Publishing
Date: 2013
DOI: 10.1063/1.4776782
Abstract: A similarity analysis is presented of the momentum field of a subsonic, plane air jet over the range of the jet-exit Reynolds number Reh (≡ Ubh/υ where Ub is the area-averaged exit velocity, h the slot height, and υ the kinematic viscosity) = 1500 − 16 500. In accordance with similarity principles, the mass flow rates, shear-layer momentum thicknesses, and integral length scales corresponding to the size of large-scale coherent eddy structures are found to increase linearly with the downstream distance from the nozzle exit (x) for all Reh. The autocorrelation measurements performed in the near jet confirmed reduced scale of the larger coherent eddies for increased Reh. The mean local Reynolds number, measured on the centerline and turbulent local Reynolds number measured in the shear-layer increases non-linearly following x1/2, and so does the Taylor microscale local Reynolds number that scales as x1/4. Consequently, the comparatively larger local Reynolds number for jets produced at higher Reh causes self-preservation of the fluctuating velocity closer to the nozzle exit plane. The near-field region characterized by over-shoots in turbulent kinetic energy spectra confirms the presence of large-scale eddy structures in the energy production zone. However, the faster rate of increase of the local Reynolds number with increasing x for jets measured at larger Reh is found to be associated with a wider inertial sub-range of the compensated energy spectra, where the −5/3 power law is noted. The downstream region corresponding to the production zone persists for longer x/h for jets measured at lower Reh. As Reh is increased, the larger width of the sub-range confirms the narrower dissipative range within the energy spectra. The variations of the dissipation rate (ɛ) of turbulent kinetic energy and the Kolmogorov (η) and Taylor (λ) microscales all obey similarity relationships, $\\varepsilon h/U_{\\rm b}^3 \\sim Re_h^3$ɛh/Ub3∼Reh3, η/h ∼ Reh−3/4, and λ/h ∼ Reh−1/2. Finally, the underlying physical mechanisms related to discernible self-similar states and flow structures due to disparities in Reh and local Reynolds number is discussed.
Publisher: Springer Science and Business Media LLC
Date: 14-09-2017
Publisher: Elsevier BV
Date: 12-2023
Publisher: Springer Science and Business Media LLC
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 23-09-2018
Publisher: Elsevier BV
Date: 04-2019
Publisher: American Geophysical Union (AGU)
Date: 04-2009
DOI: 10.1029/2009GL037666
Publisher: Springer Science and Business Media LLC
Date: 25-09-2019
Publisher: Elsevier BV
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 19-01-2018
Publisher: Springer Science and Business Media LLC
Date: 23-07-2018
Publisher: Wiley
Date: 13-03-2019
DOI: 10.1002/JOC.6037
Publisher: American Geophysical Union (AGU)
Date: 11-2007
DOI: 10.1029/2007GL031524
Publisher: Elsevier BV
Date: 04-2020
DOI: 10.1016/J.SCITOTENV.2020.136587
Abstract: The relationship between climate and human society has frequently been investigated to ascertain whether climate variability can trigger social crises (e.g., migration and armed conflicts). In the current study, statistical methods (e.g., correlation analysis and Granger Causality Analysis) are used in a systematic analysis of the potential causality of climate variability on migration and armed conflicts. Specifically, the statistical methods are applied to determine the relationships between long-term fine-grained temperature and precipitation data and contemporary social conditions, gleaned from historical documents covering the last two millennia in China's Hexi Corridor. Results found the region's reconstructed temperature to be strongly coupled with precipitation dynamics, i.e., a warming climate was associated with a greater supply of moisture, whereas a cooling period was associated with more frequent drought. A prolonged cold period tended to coincide with societal instability, such as a shift from unification towards fragmentation. In contrast, a prolonged warm period coincided with rapid development, i.e., a shift from separation to unification. The statistical significance of the causality linkages between climate variability, bio-productivity, grain yield, migration and conflict suggests that climate variability is not the direct causative agent of these phenomena, but that climate reduced food production which gradually lead to migration and conflicts. A conceptual causal model developed through this study describes the causative pathway of climate variability impacts on migration and conflicts in the Hexi Corridor. Applied to current conditions, the model suggests that steady and proactive promotion of the nation's economic buffering capacity might best address the uncertainty brought on by a range of potential future climate scenarios and their potential impacts.
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.JENVMAN.2022.114711
Abstract: Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (T
Publisher: Informa UK Limited
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 20-06-2015
Publisher: Springer Science and Business Media LLC
Date: 18-08-2018
Publisher: Wiley
Date: 14-09-2017
DOI: 10.1002/HYP.11349
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 02-2024
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.SCITOTENV.2017.11.185
Abstract: Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of in idual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 11-2019
Publisher: The Pennsylvania State University Press
Date: 12-2019
DOI: 10.5325/BULLBIBLRESE.29.4.0488
Abstract: The narrative significance of Elizabeth’s “hiding” for five months in response to her pregnancy (Luke 1:24) has not been adequately explored. Some have proposed that Elizabeth’s hiding serves to keep her pregnancy secret until the time of the angel’s annunciation to Mary. This article concurs but argues that Elizabeth’s action is also a response of faith to the angel’s prophesy regarding her pregnancy. Clues in the narrative emphasize that Elizabeth was both past her childbearing years and that she hid herself soon after the conception of her baby. As a postmenopausal woman, however, she did not have the telltale pregnancy marker of a missed menstrual period. Thus, she hid herself before she had any physical evidence of pregnancy. Elizabeth, then, acted on her belief that “there will be a fulfillment of the things that were said from the Lord” (1:45), setting up a contrast with her unbelieving husband and an alignment with her believing relative Mary. This is the first of many contrasts in the narrative between characters who respond with belief and those who do not. Luke’s reference to Elizabeth’s “hiding herself” for five months is not an insignificant narrative detail but one that presents Elizabeth as the first of many unexpected characters of faith in Luke’s account of the “things fulfilled among us” (1:1).
Publisher: Springer Science and Business Media LLC
Date: 24-07-2020
Publisher: Springer Science and Business Media LLC
Date: 30-05-2020
Publisher: Springer Science and Business Media LLC
Date: 25-04-2018
Publisher: MDPI AG
Date: 09-04-2019
DOI: 10.3390/EN12071365
Abstract: Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.
Publisher: IWA Publishing
Date: 08-12-2017
DOI: 10.2166/NH.2016.396
Abstract: In this study, the ability of a wavelet analysis–artificial neural network (WA-ANN) conjunction model for multi-scale monthly groundwater level forecasting was evaluated in an arid inland river basin, northwestern China. The WA-ANN models were obtained by combining discrete wavelet transformation with ANN. For WA-ANN model, three different input combinations were trialed in order to optimize the model performance: (1) ancient groundwater level only, (2) ancient climatic data, and (3) ancient groundwater level combined with climatic data to forecast the groundwater level for two wells in Zhangye basin. Based on the key statistical measures, the performance of the WA-ANN model was significantly better than ANN model. However, WA-ANN model with ancient groundwater level as its input yielded the best performance for 1-month groundwater forecasts. For 2- and 3-monthly forecasts, the performance of the WA-ANN model with integrated ancient groundwater level and climatic data as inputs was the most superior. Notwithstanding this, the WA-ANN model with only ancient climatic data as its inputs also exhibited accurate results for 1-, 2-, and 3-month groundwater forecasting. It is ascertained that the WA-ANN model is a useful tool for simulation of multi-scale groundwater forecasting in the current study region.
Publisher: Elsevier BV
Date: 05-2022
Publisher: MDPI AG
Date: 11-11-2017
DOI: 10.3390/W9110880
Publisher: Elsevier BV
Date: 2024
Publisher: Elsevier BV
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 02-10-2017
DOI: 10.1007/S10661-017-6257-Z
Abstract: The salinity stress inhibits the growth of Populus euphratica (P. euphratica), and the extent of inhibition tends to increase with a rise of salt concentration while the net photosynthesis rate, stomatal conductance, transpiration rate, and internal CO
Publisher: MDPI AG
Date: 22-06-2019
DOI: 10.3390/EN12122407
Abstract: Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 erse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for erse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.
Publisher: Springer Science and Business Media LLC
Date: 16-08-2019
Publisher: Springer Science and Business Media LLC
Date: 05-12-2018
Publisher: Springer Science and Business Media LLC
Date: 07-09-2022
Publisher: American Meteorological Society
Date: 2010
Publisher: Elsevier BV
Date: 08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 07-2022
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/6310401
Abstract: Assessment of the effects of climate change and land use/cover change (LUCC) on the flow regimes in watershed regions is a fundamental research need in terms of the sustainable water resources management and ecosocial developments. In this study, a statistical and modeling integrated method utilizing the Soil and Water Assessment Tool (SWAT) has been adopted in two watersheds of northeastern Tibetan Plateau to separate the in idual impacts of climate and LUCC on the flow regime metrics. The integrated effects of both LUCC and climate change have led to an increase in the annual streamflow in the Yingluoxia catchment (YLC) region and a decline in the Minxian catchment (MXC) region by 3.2% and 4.3% of their total streamflow, respectively. Climate change has shown an increase in streamflow in YLC and a decline in MXC region, occupying 107.3% and 93.75% of the total streamflow changes, respectively, a reflection of climatic latitude effect on streamflow. It is thus construed that the climatic factors contribute to more significant influence than LUCC on the magnitude, variability, duration, and component of the flow regimes, implying that the climate certainly dominates the flow regime changes in northeastern Tibetan Plateau.
Publisher: Springer Science and Business Media LLC
Date: 25-10-2018
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer Science and Business Media LLC
Date: 16-01-2016
DOI: 10.1007/S10661-016-5094-9
Abstract: A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (Q WL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of Q WL) rainfall Southern Oscillation Index Pacific Decadal Oscillation Index ENSO Modoki Index Indian Ocean Dipole Index and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with Q WL, yielding the best inputs defined by (month P Nino 3.0 SST Nino 4.0 SST Southern Oscillation Index (SOI) ENSO Modoki Index (EMI)) for Gowrie Creek, (month P SOI Pacific Decadal Oscillation (PDO) Indian Ocean Dipole (IOD) EMI) for Albert River, and by (month P Nino 3.4 SST Nino 4.0 SST SOI EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r (2)), Willmott's Index (d), peak deviation (P dv), and Nash-Sutcliffe coefficient (E NS). The results verified that the ELM model as more accurate than the ANN model. Inputting the best input variables improved the performance of both models where optimum ELM yielded R(2) ≈ (0.964, 0.957, and 0.997), d ≈ (0.968, 0.982, and 0.986), and MAE ≈ (0.053, 0.023, and 0.079) for Gowrie Creek, Albert River, and Mary River, respectively, and optimum ANN model yielded smaller R(2) and d and larger simulation errors. When all inputs were utilized, simulations were consistently worse with R (2) (0.732, 0.859, and 0.932 (Gowrie Creek), d (0.802, 0.876, and 0.903 (Albert River), and MAE (0.144, 0.049, and 0.222 (Mary River) although they were relatively better than using the month and rainfall as inputs. Also, with the best input combinations, the frequency of simulation errors fell in the smallest error bracket. Therefore, it can be ascertained that the ELM model offered an efficient approach for the streamflow simulation and, therefore, can be explored for its practicality in hydrological modeling.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Informa UK Limited
Date: 04-04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 06-2022
Publisher: MDPI AG
Date: 05-09-2022
DOI: 10.3390/SU141711070
Abstract: Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score WS (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the WS, 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the WS with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on WS clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts.
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 12-2022
Publisher: American Society of Civil Engineers (ASCE)
Date: 2018
Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.SCITOTENV.2019.03.496
Abstract: Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.
Publisher: Elsevier
Date: 2018
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 10-2023
Publisher: MDPI AG
Date: 23-10-2021
DOI: 10.3390/S21217034
Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 2018
Publisher: American Physical Society (APS)
Date: 13-06-2005
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 10-05-2016
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 11-2007
Publisher: Springer Science and Business Media LLC
Date: 22-06-2018
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 05-2020
DOI: 10.1016/J.SCITOTENV.2019.134656
Abstract: Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly ided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUC
Publisher: Elsevier BV
Date: 11-2007
Publisher: Elsevier BV
Date: 05-2012
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 31-10-2018
DOI: 10.1038/S41598-018-34550-0
Abstract: To meet the growing demands of staple crops with a strategy to develop amicable strategic measures that support efficient North Korean relief policies, it is a desirable task to accurately simulate the yield of paddy ( Oryza sativa ), an important Asian food commodity. We aim to address this with a grid-based crop simulation model integrated with satellite imagery that enables us to monitor the crop productivity of North Korea. Vegetation Indices (VIs), solar insolation, and air temperature data are thus obtained from the Communication Ocean and Meteorological Satellite (COMS), including the reanalysis data of the Korea Local Analysis and Prediction System (KLAPS). Paddy productivities for North Korea are projected based on the bidirectional reflectance distribution function-adjusted VIs and the solar insolation using the grid GRAMI-rice model. The model is calibrated on a 500-m grid paddy field in Cheorwon, and the model simulation performance accuracy is verified for Cheorwon and Paju, located at the borders of North Korea using four years of data from 2011 to 2014. Our results show that the paddy yields are reproduced reasonably accurately within a statistically significant range of accuracy, in comparison with observation data in Cheorwon ( p = 0.183), Paju ( p = 0.075), and NK ( p = 0.101) according to a statistical t -test procedure. We advocate that incorporating a crop model with satellite images for crop yield simulations can be utilised as a reliable estimation technique for the monitoring of crop productivity, particularly in unapproachable, data-sparse regions not only in North Korea, but globally, where estimations of paddy productivity can assist in planning of agricultural activities that support regionally amicable food security strategies.
Publisher: MDPI AG
Date: 25-02-2022
DOI: 10.3390/RS14051136
Abstract: Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.
Publisher: Springer Science and Business Media LLC
Date: 14-10-2017
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.ENVRES.2017.01.035
Abstract: Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ
Publisher: Springer Science and Business Media LLC
Date: 04-01-2017
Publisher: MDPI AG
Date: 18-05-2023
DOI: 10.20944/PREPRINTS202305.1323.V1
Abstract: Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved & % classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.
Publisher: Elsevier BV
Date: 08-2007
Publisher: Springer Science and Business Media LLC
Date: 23-05-2016
Publisher: Elsevier BV
Date: 2018
Publisher: MDPI AG
Date: 08-07-2020
DOI: 10.3390/EN13143517
Abstract: This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).
Publisher: Springer Science and Business Media LLC
Date: 23-08-2019
Publisher: Springer Science and Business Media LLC
Date: 28-02-2019
DOI: 10.1038/S41598-019-39626-Z
Abstract: At an ecosystem level, stand age has a significant influence on carbon storage (CS). Dragon spruce ( Picea asperata Mast.) situated along the upper reaches of the Bailongjiang River in northwest China were categorized into three age classes (29–32 years, Y 1 34–39 years, Y 2 40–46 years, Y 3 ), and age-related differences in total carbon storage (TCS) of the forest ecosystem were investigated for the first time. Results showed that TCS for the Y 1 , Y 2 , and the Y 3 age groups were 323.64, 240.66 and 174.60 Mg ha −1 , respectively. The average TCS of the three age groups was 255.65 Mg C ha −1 , with above-ground biomass, below-ground biomass, litter, and soil in the top 0.6 m contributing 15.0%, 3.7%, 12.1%, and 69.2%, respectively. CS in soil and TCS of the Y 1 age group both significantly exceeded those of the Y 3 age group ( P 0.05). Contrary to other recent findings, the present study supports the hypothesis that TCS is likely to decrease as stand age increases. This indicates that natural resource managers should rejuvenate forests by routinely thinning older stands, thereby not only achieving vegetation restoration, but also allowing these stands to create a long-term carbon sink for this important eco-region.
Publisher: Elsevier BV
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 09-10-2019
Publisher: Springer Science and Business Media LLC
Date: 27-02-2018
Publisher: Informa UK Limited
Date: 13-09-2017
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 06-04-2022
DOI: 10.1007/S00477-022-02188-0
Abstract: Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD . The models developed with cloud inputs yield accurate results, outperforming the SZA -based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m −2 s −1 , mean absolute error MAE = 150.24 μ mol of photons m −2 s −1 , including a relative error of RRMSE = 24.92% MAP E = 38.01%, and Nash Sutcliffe’s coefficient E NS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.
Publisher: Springer Science and Business Media LLC
Date: 24-09-2016
Publisher: IWA Publishing
Date: 07-12-2018
DOI: 10.2166/WS.2017.236
Abstract: This study investigated the physiological response of Populus euphratica (P. euphratica) to ecological water transport. Results showed significant increases in net photosynthetic (32.71%), stomatal conductance (27.58%), and transpiration (25.18%) rates of P. euphratica prior to the ecological water transport treatment. Internal CO2 concentrations (Ci) decreased significantly compared with the day preceding the treatment (23.69% P & 0.05). During the treatment, the O, J, I, and P steps quickly increased, with the P step exhibiting the most significant change (P & 0.05). Moreover, Fv /Fm and Fv/Fo values were highest 7 d after the ecological water transport treatment. During the treatment, the initial fluorescence (F0), the maximal fluorescence intensity (Fm), PI, and RC/CSo quickly increased, with an increasing percentage of 9.67%, 46.15%, 59.17%, and 48.54%. In contrast, Vj, ABS/RC, TRo/RC, and ETo/RC rapidly decreased, with a decreasing percentage of 30.43%, 43.54%, 37.50%, and 39.04%, respectively. After the treatment, the average chlorophyll content of a, b, and a + b increased by 26.36%, 8.89%, and 21.93%, respectively, compared with the day preceding the treatment. This study also found that the relationship between soil water content and the net photosynthetic rate, stomatal conductance, the transpiration rate, the internal CO2 concentration, Fv/Fm, and Fv/Fo of P. euphratica were strongest during ecological water transport.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 21-11-2019
Publisher: MDPI AG
Date: 15-10-2019
DOI: 10.3390/W11102138
Abstract: An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a erse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).
Publisher: Elsevier BV
Date: 06-2018
Publisher: Wiley
Date: 24-05-2011
DOI: 10.1002/MET.216
Publisher: Springer Science and Business Media LLC
Date: 09-01-2020
Publisher: Springer Science and Business Media LLC
Date: 04-07-2018
DOI: 10.1007/S10661-018-6806-0
Abstract: A water resources index based on weight-accumulated precipitation over the passage of time in heavy rainfall events is used in this study for monitoring flood risk and peak danger, as well as to develop flood warnings. In this research, an hourly water resources index (WRI
Publisher: Wiley
Date: 04-08-2009
Publisher: Elsevier BV
Date: 12-2019
Publisher: Wiley
Date: 12-2017
DOI: 10.1111/SUM.12387
Publisher: Springer Science and Business Media LLC
Date: 09-09-2016
Publisher: Springer Science and Business Media LLC
Date: 20-10-2020
Publisher: MDPI AG
Date: 02-03-2023
DOI: 10.3390/S23052753
Abstract: Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data.
Publisher: Wiley
Date: 09-2019
DOI: 10.1002/ECS2.2851
Abstract: Slope aspect can affect soil temperature and soil type distribution, which, in turn, is likely to influence plant community composition. Three Qilian mountains, located in the northeastern part of the Qinghai–Tibetan Plateau, China, with four distinct slope aspects including south‐facing ( SF ), southwest‐facing ( SW ), northwest‐facing ( NW ), and north‐facing ( NF ) slope aspects, were studied to investigate the impact of slope aspect on plant assemblages. The results indicated that the environmental conditions were favorable under the NF and NW slope aspects as the soil water, soil organic carbon ( SOC ), and soil total nitrogen ( STN ) contents were significantly higher, and soil temperature ( ST ) and soil bulk density ( SBD ) were significantly lower than under the SF and SW aspects. Under all slope aspects, however, SOC , STN , and soil total phosphate in the top 0.2 m of topsoil accounted for about 60% of its total quantity, to a soil depth of 0.6 m. The plant communities on the SF and SW slopes were found to be primarily composed of Poa pratensis , Potentilla anrisena , and Carex aridula . In contrast, the plant community on the NW slope was mainly composed of Kobresia humilis , Carex crebra , and Potentilla bifurca , while on the NF slope it was mainly composed of Picea crassifolia , Carex scabrirostris, and Polygonum macrophyllum . The order of the influence of environmental factors on species distributions was ST SBD sand STN . Results suggest that the slope aspect has an important role in the regulation of the soil environment and plant assemblages and that ST and SBD were the main factors influencing plant community composition. Furthermore, evidence from this study suggests that these mountains will become increasingly vulnerable to global warming. Thus, the plant community composition on these mountains must be monitored continuously in order to allow for strategic adaptive management.
Publisher: Elsevier BV
Date: 02-2013
Publisher: MDPI AG
Date: 24-02-2017
DOI: 10.3390/W9030159
Publisher: Elsevier BV
Date: 02-2017
Publisher: Informa UK Limited
Date: 10-02-2017
Publisher: Wiley
Date: 23-12-2020
DOI: 10.1002/JOC.6435
Publisher: Wiley
Date: 09-10-2019
DOI: 10.1002/FES3.151
Publisher: Springer Science and Business Media LLC
Date: 12-2017
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 2018
Publisher: AIP Publishing
Date: 27-05-2005
DOI: 10.1063/1.1928667
Abstract: Turbulent free jets issuing from rectangular slots with various high aspect ratios (15–120) are characterized. The centerline mean and rms velocities are measured using hot-wire anemometry over a downstream distance of up to 160 slot heights at a slot-height-based Reynolds number of 10 000. Experimental results suggest that a rectangular jet with sufficiently high aspect ratio (& ) may be distinguished between three flow zones: an initial quasi-plane-jet zone, a transition zone, and a final quasi-axisymmetric-jet zone. In the quasi-plane-jet zone, the turbulent velocity field is statistically similar, but not identical, to those of a plane jet.
Publisher: EDP Sciences
Date: 2018
DOI: 10.1051/E3SCONF/20186408001
Abstract: This paper has adopted six daily climate variables for the eleven major locations, and heavily populated areas in Queensland, Australia obtained from Scientific Information for Land Owners (SILO) to forecast the daily electricity demand ( G ) obtained from the Australian Energy Market Operator (AEMO). Optimal data-driven technique based on a support vector regression (SVR) model was applied in this study for the G forecasting, where the model’s parameters were selected using a particle swarm optimization (PSO) algorithm. The performance of PSO–SVR was compared with multivariate adaptive regression spline (MARS) and the traditional model of SVR. The results showed that the PSO–SVR model outperformed MARS and SVR.
Publisher: IEEE
Date: 11-2016
Publisher: MDPI AG
Date: 31-01-2022
DOI: 10.3390/EN15031061
Abstract: We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.
Publisher: Wiley
Date: 07-2006
DOI: 10.1002/ASL.131
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 10-2018
Publisher: Springer Science and Business Media LLC
Date: 05-2018
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.SCITOTENV.2018.08.221
Abstract: Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012-May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.
Publisher: Springer Science and Business Media LLC
Date: 12-01-2021
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 23-02-2018
Publisher: Springer Science and Business Media LLC
Date: 05-2019
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 11-2018
Publisher: MDPI AG
Date: 06-05-2020
DOI: 10.3390/EN13092307
Abstract: To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional c uses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two c uses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.
Publisher: Elsevier BV
Date: 07-2018
Publisher: MDPI AG
Date: 05-05-2017
DOI: 10.3390/SU9050756
Publisher: Springer Science and Business Media LLC
Date: 07-03-2016
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer International Publishing
Date: 2017
Publisher: MDPI AG
Date: 23-11-2018
DOI: 10.3390/W10121710
Abstract: Sustainable land management requires a clear understanding of the changes in soil quality. In exploring whether afforestation has the potential to improve the soil quality in China’s Loess Plateau, soil bulk density ( ρ s ) and pH were compared under five treatments: three forested treatments (16-and 40-year-old apricot stands, and 40-year-old poplar stands), and in idual abandoned and cultivated treatments, serving as the controls. Bulk density across the 0–1.0 m soil profile under the 16-year-old apricot treatment (1.12 Mg m−3) and 40-year-old poplar treatment (1.16 Mg m−3) were significantly smaller than their counterparts under the cultivated (1.20 Mg m−3) and abandoned treatments (1.23 Mg m−3). Soil pH of the cultivated treatment (8.46) was significantly lower than that of the abandoned treatment (8.51) or than that of any forested treatment. The ρ s and pH were both affected by stand age, with the ρ s and pH of the 40-year-old apricot treatment being 0.10 Mg m−3 and 0.05 units greater, respectively, than those of the 16-year-old apricot treatment. Treatment and soil depth appeared to interact to influence the ρ s , but this same interaction did not influence the soil pH. This study suggested that afforestation species and stand age should be taken into consideration to harvest maximum benefits from the afforestation efforts.
Publisher: Elsevier BV
Date: 02-2015
Publisher: Elsevier BV
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 21-09-2018
Publisher: Elsevier BV
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 18-04-2019
Publisher: AIP Publishing
Date: 07-2008
DOI: 10.1063/1.2959171
Abstract: The present study systematically investigates through experiments the influence of Reynolds number on a plane jet issuing from a radially contoured, rectangular slot nozzle of large aspect ratio. Detailed velocity measurements were performed for a jet exit Reynolds number spanning the range 1500≤Reh≤16 500, where Reh≡Ubh/υ with Ub as the momentum-averaged exit mean velocity, h as the slot height, and υ as the kinematic viscosity. Additional centerline measurements were also performed for jets from two different nozzles in the same facility to achieve Reh=57 500. All measurements were conducted using single hot-wire anemometry to an axial distance (x) of x≤160h. These measurements revealed a significant dependence of the exit and the downstream flows on Reh despite all exit velocity profiles closely approximating a “top-hat” shape. The effect of Reh on both the mean and turbulent fields is substantial for Reh& 000 but becomes weaker with increasing Reh. The length of the jet’s potential core, initial primary-vortex shedding frequency, and far-field rates of decay and spread all depend on Reh. The local Reynolds number, Rey0.5≡2Ucy0.5/υ, where Uc and y0.5 are the local centerline velocity and half-width, respectively, are found to scale as Rey0.5∼x1/2. It is also shown that, for Reh≥1500, self-preserving relations of both the turbulence dissipation rate (ε) and smallest scale (η), i.e., ε∼Reh3(x/h)−5/2 and η∼Reh−3/4(x/h)5/8, become valid for x/h≥20.
Publisher: Springer Science and Business Media LLC
Date: 24-10-2023
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 02-2018
Publisher: Wiley
Date: 17-01-2017
DOI: 10.1002/HYP.11098
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 05-06-2017
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 16-10-2019
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 03-2023
Publisher: Springer Science and Business Media LLC
Date: 07-2017
Publisher: MDPI AG
Date: 04-02-2021
DOI: 10.3390/RS13040554
Abstract: Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.
Publisher: Springer Science and Business Media LLC
Date: 08-2016
Publisher: Elsevier BV
Date: 03-2020
DOI: 10.1016/J.SCITOTENV.2019.135934
Abstract: Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM
Publisher: Springer Science and Business Media LLC
Date: 26-02-2019
Publisher: Informa UK Limited
Date: 27-03-2020
Publisher: Springer Science and Business Media LLC
Date: 21-03-2019
Location: No location found
Location: Fiji
Location: Australia
Start Date: 04-2011
End Date: 03-2015
Amount: $706,512.00
Funder: Australian Research Council
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