Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study
Environmental Modelling & Software
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Input selection for nonlinear regression models
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks
Environmental Modelling & Software
Application of chaos and fractal models to water quality time series prediction
Environmental Modelling & Software
Environmental Modelling & Software
Data-driven dynamic emulation modelling for the optimal management of environmental systems
Environmental Modelling & Software
AMI Screening Using Linguistic Fuzzy Rules
Journal of Medical Systems
Environmental Modelling & Software
Computers and Electronics in Agriculture
Automatic generation of water distribution systems based on GIS data
Environmental Modelling & Software
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Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development. This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables.