ORCID Profile
0000-0002-0277-6887
Current Organisation
University of Adelaide
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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)
Natural Resource Management | Neural Networks, Genetic Alogrithms And Fuzzy Logic | Ecology | Environmental Science and Management | Artificial Intelligence and Image Processing | Freshwater Ecology | Water And Sanitary Engineering | Water Resources Engineering | Surfacewater Hydrology | Simulation And Modelling | Agriculture, Land and Farm Management | Physical Geography and Environmental Geoscience | Environmental Engineering Design | Chemical Engineering | Environmental Engineering Modelling | Applied Hydrology (Drainage, Flooding, Irrigation, Quality, Etc.) | Sustainable Development | Environmental Management And Rehabilitation | Membrane And Separation Technologies | Marine And Estuarine Ecology (Incl. Marine Ichthyology) | Landscape Ecology | Conservation And Biodiversity | Hydrology Not Elsewhere Classified |
ANZSRC Socio-Economic Objective (SEO)
Land and water management | Integrated (ecosystem) assessment and management | Land and water management | Land and water management | Application tools and system utilities | Integrated (ecosystem) assessment and management | Land and water management | Integrated (ecosystem) assessment and management | Estuarine and lagoon areas | Biological sciences | Environmental and resource evaluation not elsewhere classified | Water Allocation and Quantification | Urban and Industrial Water Management | Living resources (flora and fauna) | Land and water management | Land and water management | Water services and utilities | Global climate change adaptation measures | Farmland, Arable Cropland and Permanent Cropland Water Management
Related Links
Publications
Effect of Storage Tank Size on the Minimization of Water Distribution System Cost and Greenhouse Gas Emissions While Considering Time-Dependent Emissions Factors
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2016
Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation
Publisher: Elsevier BV
Date: 09-2015
Achieving Robust and Transferable Performance for Conservation‐Based Models of Dynamical Physical Systems
Publisher: American Geophysical Union (AGU)
Date: 05-2022
DOI: 10.1029/2021WR031818
Abstract: Because physics‐based models of dynamical systems are constrained to obey conservation laws, they must typically be fed long sequences of temporally consecutive (TC) data during model calibration and evaluation. When memory time scales are long (as in many physical systems), this requirement makes it difficult to ensure distributional similarity when partitioning the data into independent, TC, calibration and evaluation subsets. The consequence can be poor and/or uncertain model performance when applied to new situations. To address this issue, we propose a novel strategy for achieving robust and transferable model performance. Instead of partitioning the data into TC calibration and evaluation periods, the model is run in continuous simulation mode for the entire period, and specific time steps are assigned (via a deterministic data‐allocation approach) for use in computing the calibration and evaluation metrics. Generative adversarial testing shows that this approach results in consistent calibration and evaluation data subset distributions. When tested using three conceptual rainfall‐runoff models applied to 163 catchments representing a wide range of hydro‐climatic conditions, the proposed “ distributionally consistent (DC)” strategy consistently resulted in better overall performance than achieved using the traditional “TC” strategy. Testing on independent data periods confirmed superior robustness and transferability of the DC‐calibrated models, particularly under conditions of larger runoff skewness. Because the approach is generally applicable to physics‐based models of dynamical systems, it has the potential to significantly improve the confidence associated with prediction and uncertainty estimates generated using such models.
Framework for computationally efficient optimal crop and water allocation using ant colony optimization
Publisher: Elsevier BV
Date: 02-2016
Selection of smoothing parameter estimators for general regression neural networks – Applications to hydrological and water resources modelling
Publisher: Elsevier BV
Date: 09-2014
Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia
Publisher: Elsevier BV
Date: 1998
Data division for developing neural networks applied to geotechnical engineering
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2004
Optimal sequencing of water supply options at the regional scale incorporating alternative water supply sources and multiple objectives
Publisher: Elsevier BV
Date: 03-2014
A computational software tool for the minimization of costs and greenhouse gas emissions associated with water distribution systems
Publisher: Elsevier BV
Date: 07-2015
Reliability-based approach to multicriteria decision analysis for water resources
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2004
Improved selection strategy for multi‐objective evolutionary algorithms with application to water distribution optimization problems
Publisher: Wiley
Date: 25-01-2023
DOI: 10.1111/MICE.12964
Abstract: Multi‐objective evolutionary algorithms (MOEAs) have been applied to water distribution system (WDS) optimization problems for over two decades. The selection strategy is a key component of an MOEA that determines the composition of a population, and thereby the evolutionary search process, which imitates natural selection by granting fitter in iduals an increasing opportunity to reproduce. This paper proposes the convex hull contribution (CHC) selection strategy for generational MOEAs (CHC Gen ) as a novel selection strategy that is based on the CHC of solutions to the Pareto front in the objective space. Numerical experiments using a general MOEA framework demonstrate that the CHC Gen selection strategy is able to outperform existing popular selection strategies (e.g., crowding distance, hypervolume contribution, and hybrid replacement selection). Moreover, it is illustrated that the CHC Gen selection strategy is able to improve the performance of existing MOEAs such as NSGA‐II and GALAXY. The conclusions are based on the results of six bi‐objective WDS problems.
Advection, growth and nutrient status of phytoplankton populations in the lower River Murray, South Australia
Publisher: Wiley
Date: 2000
DOI: 10.1002/1099-1646(200007/08)16:4<327::AID-RRR576>3.0.CO;2-Q
On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization
Publisher: Elsevier BV
Date: 09-2023
Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Publisher: Elsevier BV
Date: 09-2023
Forecasting cyanobacterium Anabaena spp. in the River Murray, South Australia, using B-spline neurofuzzy models
Publisher: Elsevier BV
Date: 12-2001
Integrated approach for optimizing the design of aquifer storage and recovery stormwater harvesting schemes accounting for externalities and climate change
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2016
A framework for the mitigation and adaptation from heat-related risks to infrastructure
Publisher: Elsevier BV
Date: 06-2022
Evaluation of parameter setting for two GIS based unit hydrograph models
Publisher: Elsevier BV
Date: 11-2010
Future research challenges for incorporation of uncertainty in environmental and ecological decision-making
Publisher: Elsevier BV
Date: 12-2008
Rainfall runoff modelling using neural networks: State-Of-The-Art and future research needs
Publisher: Informa UK Limited
Date: 2009
Single-Objective versus Multiobjective Optimization of Water Distribution Systems Accounting for Greenhouse Gas Emissions by Carbon Pricing
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2010
Robust optimization of water infrastructure planning under deep uncertainty using metamodels
Publisher: Elsevier BV
Date: 07-2017
An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?
Publisher: Elsevier BV
Date: 07-2016
Selecting among five common modelling approaches for integrated environmental assessment and management
Publisher: Elsevier BV
Date: 09-2013
Water distribution network reliability estimation using the First-Order Reliability Method
Publisher: American Society of Civil Engineers
Date: 15-05-2004
Thematic issue on Evolutionary Algorithms in Water Resources Preface
Publisher: Elsevier BV
Date: 07-2015
Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters
Publisher: Elsevier BV
Date: 05-2004
Incorporation of Variable-Speed Pumping in Multiobjective Genetic Algorithm Optimization of the Design of Water Transmission Systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2012
Selection of genetic algorithm parameters for water distribution system optimization
Publisher: American Society of Civil Engineers
Date: 07-2005
DOI: 10.1061/40792(173)44
Introductory overview: Optimization using evolutionary algorithms and other metaheuristics
Publisher: Elsevier BV
Date: 04-2019
A systematic approach to determining metamodel scope for risk-based optimization and its application to water distribution system design
Publisher: Elsevier BV
Date: 07-2015
Ant colony optimization for the design of water distribution systems
Publisher: American Society of Civil Engineers
Date: 15-05-2004
Physical and mental health impact of COVID-19 on children, adolescents, and their families: The Collaborative Outcomes study on Health and Functioning during Infection Times - Children and Adolescents
Publisher: Elsevier BV
Date: 02-2022
An integrated dynamic modeling framework for investigating the impact of climate change and variability on irrigated agriculture
Publisher: American Geophysical Union (AGU)
Date: 07-2011
DOI: 10.1029/2010WR010195
Multiobjective Optimization of Distributed Stormwater Harvesting Systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2017
Water distribution system optimisation accounting for a range of future possible carbon prices
Publisher: American Society of Civil Engineers
Date: 29-04-2009
DOI: 10.1061/41024(340)25
Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty
Publisher: American Geophysical Union (AGU)
Date: 03-2015
DOI: 10.1002/2014WR016254
Relationship between problem characteristics and the optimal number of genetic algorithm generations
Publisher: Informa UK Limited
Date: 04-2011
The response of freshwater plants to salinity pulses
Publisher: Elsevier BV
Date: 08-2010
Assessment of the ability to meet environmental water requirements in the Upper South East of South Australia
Publisher: Springer Science and Business Media LLC
Date: 03-05-2014
Framework for assessing and improving the performance of recursive digital filters for baseflow estimation with application to the Lyne and Hollick filter
Publisher: Elsevier BV
Date: 03-2013
A comparative study of artificial neural network techniques for river stage forecasting
Publisher: IEEE
Date: 2005
A climate stress testing method for changes in spatially variable rainfall
Publisher: Elsevier BV
Date: 10-2023
Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions
Publisher: American Geophysical Union (AGU)
Date: 12-2018
DOI: 10.1029/2018RG000616
Non-linear variable selection for artificial neural networks using partial mutual information
Publisher: Elsevier BV
Date: 10-2008
Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems
Publisher: Elsevier BV
Date: 10-2008
On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data‐Driven Models
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017WR021470
Impact of drought on adelaide’s water supply system: Past, present, and future
Publisher: Springer Netherlands
Date: 2013
Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan
Publisher: Informa UK Limited
Date: 12-04-2016
Bayesian training of artificial neural networks used for water resources modeling
Publisher: American Geophysical Union (AGU)
Date: 12-2005
DOI: 10.1029/2005WR004152
Sensitivity of Optimal Tradeoffs between Cost and Greenhouse Gas Emissions for Water Distribution Systems to Electricity Tariff and Generation
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2012
Best practices for conceptual modelling in environmental planning and management
Publisher: Elsevier BV
Date: 06-2016
The effect of inundation and salinity on the germination of seed banks from wetlands in South Australia
Publisher: Elsevier BV
Date: 02-2011
A Comprehensive Framework to Evaluate Hydraulic and Water Quality Impacts of Pipe Breaks on Water Distribution Systems
Publisher: American Geophysical Union (AGU)
Date: 10-2018
DOI: 10.1029/2018WR022736
Abstract: Pipe breaks have significant impacts on the hydraulic and water quality performance of water distribution systems (WDSs). Therefore, it is important to evaluate these impacts for developing effective strategies to ultimately minimize the consequences of these events. However, there has been surprisingly limited research focusing on impact evaluation for pipe breaks so far. To address this gap, this paper proposes a framework to comprehensively evaluate hydraulic and water quality impacts of pipe breaks on a WDS using six quantitative metrics. These metrics primarily focus on identifying (i) break outflow volume, (ii) water shortage, (iii) nodes with reduced service quality, (iv) pipes with affected pressures, (v) pipes with reversed flow directions, and (vi) pipes with significantly increased velocities, for each breaking event within a WDS. Statistical behaviors, spatial properties, and pipe rankings of metric results are analyzed to reveal the underlying characteristics of impacts induced by pipe breaks. We illustrate the proposed framework using three WDSs with different properties. Results show that impacts of pipe breaks not only vary with pipe diameters but are also significantly influenced by pipe locations, when the break occurs, and the specific metric considered. The proposed framework greatly enhances the fundamental understanding of the underlying properties of breaking impacts on the hydraulic and water quality of WDSs, as well as the ranking of pipes based on the consequences of breaks. Such understanding offers important guidance to develop effective pipe management, resource planning, and break restoration strategies to minimize the impacts of breaking events on WDSs.
Framework for minimising the impact of regional shocks on global food security using multi-objective ant colony optimisation
Publisher: Elsevier BV
Date: 09-2017
Ant colony optimization for power plant maintenance scheduling optimization - a five-station hydropower system
Publisher: Springer Science and Business Media LLC
Date: 12-2008
Impact of Scenario Selection on Robustness
Publisher: American Geophysical Union (AGU)
Date: 27-08-2020
DOI: 10.1029/2019WR026515
Abstract: Multiple plausible future scenarios are being used increasingly in preference to a single deterministic or probabilistic prediction of the future in the long‐term planning of water resources systems. These scenarios enable the determination of the robustness of a system—the consideration of performance across a range of plausible futures—and allow an assessment of which possible future system configurations result in a greater level of robustness. There are many approaches to selecting scenarios, and previous studies have observed that the choice of scenarios might affect the estimated robustness of the system. However, these observations have been anecdotal and qualitative. This paper develops a systematic, quantitative methodology for exploring the influence of scenario selection on the robustness and the ranking of decision alternatives. The methodology is illustrated on the Lake Problem. The quantitative results obtained confirm the qualitative observations of previous works, showing that the selection of scenarios is important, as it has a large influence on the robustness value calculated for each decision alternative. However, we show that it has a relatively small influence on how those decision alternatives are ranked. This implies that despite the difference in robustness values, similar decision outcomes will be reached in this case study, regardless of the basis on which the scenarios are obtained. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics.
Including adaptation and mitigation responses to climate change in a multiobjective evolutionary algorithm framework for urban water supply systems incorporating GHG emissions
Publisher: American Geophysical Union (AGU)
Date: 08-2014
DOI: 10.1002/2013WR015195
A tribute in memory of Dr. James (Jim) C. Ascough II
Publisher: Elsevier BV
Date: 11-2017
Risk-based approach for assessing the effectiveness of flow management in controlling cyanobacterial blooms in rivers
Publisher: Wiley
Date: 07-2004
DOI: 10.1002/RRA.760
Real-Time, Smart Rainwater Storage Systems: Potential Solution to Mitigate Urban Flooding
Publisher: MDPI AG
Date: 20-11-2019
DOI: 10.3390/W11122428
Abstract: Urban water systems are being stressed due to the effects of urbanization and climate change. Although household rainwater tanks are primarily used for water supply purposes, they also have the potential to provide flood benefits. However, this potential is limited for critical storms, as they become ineffective once their capacity is exceeded. This limitation can be overcome by controlling tanks as systems during rainfall events, as this can offset the timing of outflow peaks from different tanks. In this paper, the effectiveness of such systems is tested for two tank sizes under a wide range of design rainfall conditions for three Australian cities with different climates. Results show that a generic relationship exists between the ratio of tank:runoff volume and percentage peak flow reduction, irrespective of location and storm characteristics. Smart tank systems are able to reduce peak system outflows by between 35% and 85% for corresponding ranges in tank:runoff volumes of 0.15–0.8. This corresponds to a relative performance improvement on the order of 35% to 50% compared with smart tanks that are not operated in real-time. These results highlight the potential for using household rainwater tanks for mitigating urban flooding, even for extreme events.
Multiobjective optimization of cluster-scale urban water systems investigating alternative water sources and level of decentralization
Publisher: American Geophysical Union (AGU)
Date: 10-2014
DOI: 10.1002/2013WR015233
Robust staged development of water supply systems
Publisher: Elsevier BV
Date: 2014
Data splitting for artificial neural networks using SOM-based stratified sampling
Publisher: Elsevier BV
Date: 03-2010
DOI: 10.1016/J.NEUNET.2009.11.009
Abstract: Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate s le size, the s ling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance however, the choice of s ling methodology is rarely given due consideration by ANN modellers. Increased confidence in the s ling is of paramount importance, since the hold-out s ling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified s ling, based on Neyman s ling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and s le allocation in order to minimize the bias and variance in the datasets. Using an ex le ANN function approximation task, the SOM-based approach is evaluated in comparison to random s ling, DUPLEX, systematic stratified s ling, and trial-and-error s ling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality s les and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets.
Ant Colony Optimization Applied to Water Distribution System Design: Comparative Study of Five Algorithms
Publisher: American Society of Civil Engineers (ASCE)
Date: 2007
Optimal Operation of Complex Water Distribution Systems Using Metamodels
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2010
Complete Bypass of Restriction Systems for Major Staphylococcus aureus Lineages.
Publisher: American Society for Microbiology
Date: 07-2015
Abstract: Staphylococcus aureus is a prominent global nosocomial and community-acquired bacterial pathogen. A strong restriction barrier presents a major hurdle for the introduction of recombinant DNA into clinical isolates of S. aureus . Here, we describe the construction and characterization of the IMXXB series of Escherichia coli strains that mimic the type I adenine methylation profiles of S. aureus clonal complexes 1, 8, 30, and ST93. The IMXXB strains enable direct, high-efficiency transformation and streamlined genetic manipulation of major S. aureus lineages. IMPORTANCE The genetic manipulation of clinical S. aureus isolates has been h ered due to the presence of restriction modification barriers that detect and subsequently degrade inappropriately methylated DNA. Current methods allow the introduction of plasmid DNA into a limited subset of S. aureus strains at high efficiency after passage of plasmid DNA through the restriction-negative, modification-proficient strain RN4220. Here, we have constructed and validated a suite of E. coli strains that mimic the adenine methylation profiles of different clonal complexes and show high-efficiency plasmid DNA transfer. The ability to bypass RN4220 will reduce the cost and time involved for plasmid transfer into S. aureus . The IMXXB series of E. coli strains should expedite the process of mutant construction in erse genetic backgrounds and allow the application of new techniques to the genetic manipulation of S. aureus .
An evaluation framework for input variable selection algorithms for environmental data-driven models
Publisher: Elsevier BV
Date: 12-2014
Neural network based modelling of environmental variables: A systematic approach
Publisher: Elsevier BV
Date: 03-2001
Energy recovery strategies in water distribution networks: literature review and future directions in the net-zero transition
Publisher: Informa UK Limited
Date: 31-05-2023
Erratum to Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge [J. Water Resour. Plann. Manage., (2016), 04016025]
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2016
On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation
Publisher: American Geophysical Union (AGU)
Date: 03-2020
DOI: 10.1029/2019WR026752
Including stakeholder input in formulating and solving real-world optimisation problems: Generic framework and case study
Publisher: Elsevier BV
Date: 05-2016
The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study
Publisher: Elsevier BV
Date: 04-1998
An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks
Publisher: American Geophysical Union (AGU)
Date: 11-2013
DOI: 10.1002/2012WR012713
Relative magnitudes of sources of uncertainty in assessing climate change impacts on water supply security for the southern Adelaide water supply system
Publisher: American Geophysical Union (AGU)
Date: 03-2013
DOI: 10.1002/WRCR.20153
Ant Colony Optimization for Design of Water Distribution Systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2003
Calibration and Optimization of the Pumping and Disinfection of a Real Water Supply System
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2010
Sensitivity analysis for decision-making using the MORE method—A Pareto approach
Publisher: Elsevier BV
Date: 07-2009
Accounting for Greenhouse Gas Emissions in Multiobjective Genetic Algorithm Optimization of Water Distribution Systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2010
Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 2004
Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2016
Using characteristics of the optimisation problem to determine the Genetic Algorithm population size when the number of evaluations is limited
Publisher: Elsevier BV
Date: 07-2015
Distance-based and stochastic uncertainty analysis for multi-criteria decision analysis in Excel using Visual Basic for Applications
Publisher: Elsevier BV
Date: 12-2006
Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models
Publisher: Elsevier BV
Date: 12-2003
Application of natural computing (computational intelligence) methods to water resources and environmental modelling
Publisher: Elsevier BV
Date: 09-2006
A New Algorithm for Water Distribution System Optimization: Discrete Dynamically Dimensioned Search
Publisher: American Society of Civil Engineers
Date: 05-2008
First‐order reliability method for estimating reliability, vulnerability, and resilience
Publisher: American Geophysical Union (AGU)
Date: 03-2001
DOI: 10.1029/2000WR900329
Estimating risk measures for water distribution systems using metamodels
Publisher: American Society of Civil Engineers
Date: 07-2005
DOI: 10.1061/40792(173)17
Optimising the mutual information of ecological data clusters using evolutionary algorithms
Publisher: Elsevier BV
Date: 09-2006
A framework for using ant colony optimization to schedule environmental flow management alternatives for rivers, wetlands, and floodplains
Publisher: American Geophysical Union (AGU)
Date: 08-2012
DOI: 10.1029/2011WR011276
Scenario driven optimal sequencing under deep uncertainty
Publisher: Elsevier BV
Date: 06-2015
Application of two ant colony optimisation algorithms to water distribution system optimisation
Publisher: Elsevier BV
Date: 09-2006
Forecasting chlorine residuals in a water distribution system using a general regression neural network
Publisher: Elsevier BV
Date: 09-2006
Input determination for neural network models in water resources applications. Part 1 - background and methodology
Publisher: Elsevier BV
Date: 2005
Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017EF000649
Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods
Publisher: Elsevier BV
Date: 09-2006
Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river
Publisher: Elsevier BV
Date: 2005
What constitutes a good literature review and why does its quality matter?
Publisher: Elsevier BV
Date: 05-2013
Integrated framework for assessing urban water supply security of systems with non-traditional sources under climate change
Publisher: Elsevier BV
Date: 10-2014
A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological prediction
Publisher: Elsevier BV
Date: 09-2006
Impact of evapotranspiration process representation on runoff projections from conceptual rainfall‐runoff models
Publisher: American Geophysical Union (AGU)
Date: 2017
DOI: 10.1002/2016WR019627
Achieving Water Quality System Reliability Using Genetic Algorithms
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2000
Calibration of rainfall runoff models in ungauged catchments: Regionalization relationships for a rainfall runoff model
Publisher: American Society of Civil Engineers
Date: 05-2008
A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models
Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022WR033703
Abstract: It is typical to use a single portion of the available data to calibrate hydrological models, and the remainder for model evaluation. To minimize model‐bias, this partitioning must be performed so as to ensure distributional representativeness and mutual consistency. However, failure to account for data s ling variability (DSV) in the underlying Data Generating Process can weaken the model's generalization performance. While “ K‐fold cross‐validation ” can mitigate this problem, it is computationally inefficient since the calibration/evaluation operations must be repeated numerous times. This paper develops a general strategy for stochastic evolutionary parameter optimization (SEPO) that explicitly accounts for DSV when calibrating a model using any population‐based evolutionary optimization algorithm (EOA), such as Shuffled Complex Evolution (SCE). Inspired in part by the machine‐learning strategy of stochastic gradient descent (SGD), we use various representative random sub‐s les to drive the EOA toward the distribution of the model parameters. Unlike in SGD, derivative information is not required and hence SEPO can be applied to any hydrological model where such information is not readily available. To demonstrate the effectiveness of the proposed strategy, we implement it within the well‐known SCE, to calibrate the GR4J conceptual rainfall‐runoff model to 163 hydro‐climatically erse catchments. Using only a single optimization run, our Stochastic SCE method converges to population‐based estimates of model parameter distributions (and corresponding simulation uncertainties), without compromising model performance during either calibration or evaluation. Further, it effectively reduces the need to perform independent evaluation tests of model performance under conditions that are represented by the available data.
Assessment of the internal dynamics of the Australian Water Balance Model under different calibration regimes
Publisher: Elsevier BV
Date: 04-2015
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
Publisher: Elsevier BV
Date: 04-2014
A modified Sobol' sensitivity analysis method for decision-making in environmental problems
Publisher: Elsevier BV
Date: 2016
Real‐time deployment of artificial neural network forecasting models: Understanding the range of applicability
Publisher: American Geophysical Union (AGU)
Date: 10-2012
DOI: 10.1029/2012WR011984
Abstract: When an operational artificial neural network (ANN) model is deployed, new input patterns are collected in order to make real‐time forecasts. However, ANNs (like other empirical and statistical methods) are unable to reliably extrapolate beyond the calibration range. Consequently, when deployed in real‐time operation there is a need to determine if new input patterns are representative of the data used in calibrating the model. To address this problem, a novel detection system for identifying uncharacteristic data patterns is presented. This approach combines a self‐organizing map (SOM), to partition the data set, with nonparametric kernel density estimators to calculate local density estimates (LDE). The SOM‐LDE method determines the degree to which a new input pattern can be considered to be contained within the domain of the calibration set. If a new pattern is found to be uncharacteristic, a warning can be issued with the forecast, and the ANN model retrained to include the new pattern. This approach of selectively retraining the model is compared to no retraining and the more computationally onerous case of retraining the model after each new s le. These three approaches are applied to forecast flow in the Kentucky River, USA, using multilayer perceptron (MLP) models. The results demonstrate that there is a significant advantage in retraining an ANN that has been deployed as a real‐time, operational model, and that the SOM‐LDE classifier is an effective approach for identifying the model's range of applicability and assessing the usefulness of the forecast.
Performance assessment and improvement of recursive digital baseflow filters for catchments with different physical characteristics and hydrological inputs
Publisher: Elsevier BV
Date: 04-2014
Hybrid discrete dynamically dimensioned search (HD‐DDS) algorithm for water distribution system design optimization
Publisher: American Geophysical Union (AGU)
Date: 12-2009
DOI: 10.1029/2008WR007673
Optimal design of water distribution systems including water quality and system uncertainty
Publisher: American Society of Civil Engineers
Date: 13-03-2007
DOI: 10.1061/40941(247)82
Comparison of Genetic Algorithm Parameter Setting Methods for Chlorine Injection Optimization
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2010
An R package for modelling actual, potential and reference evapotranspiration
Publisher: Elsevier BV
Date: 04-2016
A hydraulic mixing-cell method to quantify the groundwater component of streamflow within spatially distributed fully integrated surface water-groundwater flow models
Publisher: Elsevier BV
Date: 07-2011
A modelling framework and R-package for evaluating system performance under hydroclimate variability and change
Publisher: Elsevier BV
Date: 05-2021
The determinants of household water consumption: A review and assessment framework for research and practice
Publisher: Springer Science and Business Media LLC
Date: 20-02-2023
DOI: 10.1038/S41545-022-00208-8
Abstract: Achieving a thorough understanding of the determinants of household water consumption is crucial to support demand management strategies. Yet, existing research on household water consumption determinants is often limited to specific case studies, with findings that are difficult to generalize and not conclusive. Here, we first contribute an updated framework for review, classification, and analysis of the literature on the determinants of household water consumption. Our framework allows trade-off analysis of different criteria that account for the representation of a potential water consumption determinant in the literature, its impact across heterogeneous case studies, and the effort required to collect information on it. We then review a comprehensive set of 48 publications with our proposed framework. The results of our trade-off analysis show that distinct groups of determinants exist, allowing for the formulation of recommendations for practitioners and researchers on which determinants to consider in practice and prioritize in future research.
Management Option Rank Equivalence (MORE) - A new method of sensitivity analysis for decision-making
Publisher: Elsevier BV
Date: 02-2010
An evaluation of methods for the selection of inputs for an artificial neural network based river model
Publisher: Springer-Verlag
Date: 2006
Predicting settlement of shallow foundations using neural networks
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2002
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Publisher: Copernicus GmbH
Date: 19-04-2017
DOI: 10.5194/HESS-21-2107-2017
Abstract: Abstract. Assessing the factors that have an impact on potential evapotranspiration (PET) sensitivity to changes in different climate variables is critical to understanding the possible implications of climatic changes on the catchment water balance. Using a global sensitivity analysis, this study assessed the implications of baseline climate conditions on the sensitivity of PET to a large range of plausible changes in temperature (T), relative humidity (RH), solar radiation (Rs) and wind speed (uz). The analysis was conducted at 30 Australian locations representing different climatic zones, using the Penman–Monteith and Priestley–Taylor PET models. Results from both models suggest that the baseline climate can have a substantial impact on overall PET sensitivity. In particular, approximately 2-fold greater changes in PET were observed in cool-climate energy-limited locations compared to other locations in Australia, indicating the potential for elevated water loss as a result of increasing actual evapotranspiration (AET) in these locations. The two PET models consistently indicated temperature to be the most important variable for PET, but showed large differences in the relative importance of the remaining climate variables. In particular for the Penman–Monteith model, wind and relative humidity were the second-most important variables for dry and humid catchments, respectively, whereas for the Priestley–Taylor model solar radiation was the second-most important variable, with the greatest influence in warmer catchments. This information can be useful to inform the selection of suitable PET models to estimate future PET for different climate conditions, providing evidence on both the structural plausibility and input uncertainty for the alternative models.
Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment
Publisher: Elsevier BV
Date: 11-2017
An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditions
Publisher: American Geophysical Union (AGU)
Date: 10-2014
DOI: 10.1002/2013WR015187
Neural network based stochastic design charts for settlement prediction
Publisher: Canadian Science Publishing
Date: 02-2005
DOI: 10.1139/T04-096
Abstract: Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.
A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate
Publisher: American Geophysical Union (AGU)
Date: 09-2016
DOI: 10.1002/2015WR018253
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Publisher: Elsevier BV
Date: 2000
Interpreting streamflow generation mechanisms from integrated surface-subsurface flow models of a riparian wetland and catchment
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/WRCR.20405
A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network
Publisher: Elsevier BV
Date: 09-2016
Legal and Forensic Medicine
Publisher: Springer Berlin Heidelberg
Date: 2013
Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model
Publisher: Elsevier BV
Date: 11-2017
Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models
Publisher: Elsevier BV
Date: 03-2015
Optimal Control of Total Chlorine and Free Ammonia Levels in a Water Transmission Pipeline Using Artificial Neural Networks and Genetic Algorithms
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2015
Water distribution system optimization using metamodels
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2005
Water Distribution System Pumping Operational Greenhouse Gas Emissions Minimization by Considering Time-Dependent Emissions Factors
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2015
Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem
Publisher: American Geophysical Union (AGU)
Date: 09-2012
DOI: 10.1029/2011WR011652
Abstract: Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade‐off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP‐hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near‐optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.
Ant colony optimization for power plant maintenance scheduling optimization
Publisher: ACM
Date: 25-06-2005
Forecasting Cyanobacterial Concentrations Using B-Spline Networks
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2000
Use of domain knowledge to increase the convergence rate of evolutionary algorithms for optimizing the cost and resilience of water distribution systems
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2016
Chapter Five Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions
Publisher: Elsevier
Date: 2008
A genetic algorithm calibration method based on convergence due to genetic drift
Publisher: Elsevier BV
Date: 07-2008
Review of literature on decision support systems for natural hazard risk reduction: Current status and future research directions
Publisher: Elsevier BV
Date: 10-2017
Closure to ‘predicting settlement of shallow foundations using neural networks’
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2003
A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making
Publisher: Elsevier BV
Date: 12-2005
DOI: 10.1016/J.JENVMAN.2005.06.011
Abstract: The choice among alternative water supply sources is generally based on the fundamental objective of maximising the ratio of benefits to costs. There is, however, a need to consider sustainability, the environment and social implications in regional water resources planning, in addition to economics. In order to achieve this, multi-criteria decision analysis (MCDA) techniques can be used. Various sources of uncertainty exist in the application of MCDA methods, including the selection of the MCDA method, elicitation of criteria weights and assignment of criteria performance values. The focus of this paper is on the uncertainty in the criteria weights. Sensitivity analysis can be used to analyse the effects of uncertainties associated with the criteria weights. Two existing sensitivity methods are described in this paper and a new distance-based approach is proposed which overcomes limitations of these methods. The benefits of the proposed approach are the concurrent alteration of the criteria weights, the applicability of the method to a range of MCDA techniques and the identification of the most critical criteria weights. The existing and proposed methods are applied to three case studies and the results indicate that simultaneous consideration of the uncertainty in the criteria weights should be an integral part of the decision making process.
Enhancing the policy relevance of exploratory scenarios: Generic approach and application to disaster risk reduction
Publisher: Elsevier BV
Date: 05-2018
Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions
Publisher: American Geophysical Union (AGU)
Date: 03-2013
DOI: 10.1002/WRCR.20120
Multi-objective optimization of integrated water reuse systems at a cluster scale
Publisher: American Society of Civil Engineers
Date: 21-12-2012
DOI: 10.1061/41203(425)83
Improved validation framework and R-package for artificial neural network models
Publisher: Elsevier BV
Date: 06-2017
Evaluation of outputs from automated baseflow separation methods against simulated baseflow from a physically based, surface water-groundwater flow model
Publisher: Elsevier BV
Date: 08-2012
A multi-stakeholder portfolio optimization framework applied to stormwater best management practice (BMP) selection
Publisher: Elsevier BV
Date: 11-2017
Controlling rainwater storage as a system: An opportunity to reduce urban flood peaks for rare, long duration storms
Publisher: Elsevier BV
Date: 2019
Do Existing Multiobjective Evolutionary Algorithms Use a Sufficient Number of Operators? An Empirical Investigation for Water Distribution Design Problems
Publisher: American Geophysical Union (AGU)
Date: 05-2020
DOI: 10.1029/2019WR026031
Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches
Publisher: Wiley
Date: 16-05-2022
DOI: 10.1002/WAT2.1599
Abstract: Wildfires elicit a ersity of hydrological changes, impacting processes that drive both water quantity and quality. As wildfires increase in frequency and severity, there is a need to assess the implications for the hydrological response. Wildfire‐related hydrological changes operate at three distinct timescales: the immediate fire aftermath, the recovery phase, and long‐term across multiple cycles of wildfire and regrowth. Different dominant processes operate at each timescale. Consequentially, models used to predict wildfire impacts need an explicit representation of different processes, depending on modeling objectives and wildfire impact timescale. We summarize existing data‐driven, conceptual, and physically based models used to assess wildfire impacts on runoff, identifying the dominant assumptions, process representations, timescales, and key limitations of each model type. Given the substantial observed and projected changes to wildfire regimes and associated hydrological impacts, it is likely that physically based models will become increasingly important. This is due to their capacity both to simulate simultaneous changes to multiple processes, and their use of physical and biological principles to support extrapolation beyond the historical record. Yet benefits of physically based models are moderated by their higher data requirements and lower computational speed. We argue that advances in predicting hydrological impacts from wildfire will come through combining these physically based models with new computationally faster conceptual and reduced‐order models. The aim is to combine the strengths and overcome weaknesses of the different model types, enabling simulations of critical water resources scenarios representing wildfire‐induced changes to runoff. This article is categorized under: Water and Life Conservation, Management, and Awareness Science of Water Hydrological Processes Science of Water Water and Environmental Change
Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach
Publisher: Elsevier BV
Date: 04-2009
Comparison of the Searching Behavior of NSGA-II, SAMODE, and Borg MOEAs Applied to Water Distribution System Design Problems
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2016
A generic framework for regression regionalization in ungauged catchments
Publisher: Elsevier BV
Date: 2012
Impact of starting position and searching mechanism on the evolutionary algorithm convergence rate
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2016
Surplus Power Factor as a Resilience Measure for Assessing Hydraulic Reliability in Water Transmission System Optimization
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2011
Alternative modelling approaches for the estimation of irreducible water saturation: Australian hydrocarbon basins
Publisher: Elsevier BV
Date: 05-2007
Battle of the Water Networks II
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2014
Integrating modelling and smart sensors for environmental and human health
Publisher: Elsevier BV
Date: 12-2015
Power plant maintenance scheduling using ant colony optimization: an improved formulation
Publisher: Informa UK Limited
Date: 04-2008
Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
Publisher: American Geophysical Union (AGU)
Date: 11-2019
DOI: 10.1029/2019WR024897
Identification of optimal water supply portfolios for a Major City
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2017
A multiobjective ant colony optimization approach for scheduling environmental flow management alternatives with application to the River Murray, Australia
Publisher: American Geophysical Union (AGU)
Date: 10-2013
DOI: 10.1002/WRCR.20518
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Publisher: Elsevier BV
Date: 08-2010
Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study
Publisher: Elsevier BV
Date: 04-1998
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Publisher: Elsevier BV
Date: 03-2021
Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2005
Calibration and validation of neural networks to ensure physically plausible hydrological modeling
Publisher: Elsevier BV
Date: 11-2005
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
Publisher: Elsevier BV
Date: 12-2014
Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge
Publisher: Elsevier BV
Date: 07-2015
Bayesian model selection applied to artificial neural networks used for water resources modeling
Publisher: American Geophysical Union (AGU)
Date: 04-2008
DOI: 10.1029/2007WR006155
Effectiveness of Nature-Based Solutions for Mitigating the Impact of Pluvial Flooding in Urban Areas at the Regional Scale
Publisher: MDPI AG
Date: 17-04-2023
DOI: 10.3390/W15040642
Abstract: Pluvial flooding causes significant damage in urban areas worldwide. The most common approaches to mitigating these impacts at regional scales include structural measures such as dams, levees and floodways. More recently, the use of nature-based solutions (NBS) is receiving increasing attention, as such approaches are more adaptive than structural measures and have a number of potential co-benefits (e.g., improvements in water quality and amenity). As NBSs are generally applied at house or block scales in urban areas, their potential for reducing the impacts of urban flooding at the regional scale are unknown. We introduce an approach that enables the potential of using portfolios of NBSs to reduce the impact of urban flooding to be assessed at the regional scale. This approach enables the most suitable locations for such portfolios of NBSs to be identified, as well as their effectiveness to be modeled at spatial resolutions that are commonly used for regional planning studies. The approach is applied to a case study area to the north of Adelaide, South Australia, with results obtained suggesting that there is significant potential for using strategically placed portfolios of NBSs to reduce the impact of pluvial flooding in urban areas at the regional scale.
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