Journal ArticleDOI
Review: 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
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TLDR
A systematic protocol for the development and documentation of ANN models is introduced and shows that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.Abstract:
The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.read more
Citations
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A review of artificial neural network models for ambient air pollution prediction
TL;DR: A protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models, highlighting the need for developing systematic protocols for developing powerful ANN models.
Journal ArticleDOI
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Saman Razavi,Anthony Jakeman,Andrea Saltelli,Clémentine Prieur,Bertrand Iooss,Emanuele Borgonovo,Elmar Plischke,Samuele Lo Piano,Takuya Iwanaga,William E. Becker,Stefano Tarantola,Joseph H. A. Guillaume,John D. Jakeman,Hoshin V. Gupta,Nicola Melillo,Giovanni Rabitti,Vincent Chabridon,Qingyun Duan,Xifu Sun,Stefan Smith,R. Sheikholeslami,R. Sheikholeslami,Nasim Hosseini,Masoud Asadzadeh,Arnald Puy,Arnald Puy,Sergei Kucherenko,Holger R. Maier +27 more
TL;DR: A multidisciplinary group of researchers and practitioners revisit the current status of Sensitivity analysis, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems.
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A review of the artificial intelligence methods in groundwater level modeling
TL;DR: A review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years and the reviewing and comparison of the obtained results.
Journal ArticleDOI
Neural network river forecasting through baseflow separation and binary-coded swarm optimization
TL;DR: Comparing the performance of MM against global models for nine different gaging stations in the northern United States shows that there is no evidence that MM outperform global GM for predicting the total flow, suggesting that these two objectives are intrinsically conflicting rather than compatible.
Journal ArticleDOI
Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers
TL;DR: In this paper, the authors used a multilayer percepetron (MLP) network and tree-rings to simulate groundwater level fluctuations during the past century, and the results showed that an integration of dendrochronology and ANN rendered a high degree of accuracy and efficiency in the simulation of groundwater levels.
References
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Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
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Comparison of different efficiency criteria for hydrological model assessment
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Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Journal ArticleDOI
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
TL;DR: In this article, the authors investigate the role of artificial neural networks (ANNs) in hydrology and show that ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic.
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