Name
Title | ||
|---|---|---|
Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.envsoft.2008.03.008 | Environmental Modelling and Software |
Keywords | Field | DocType |
water quality,water supply,ann forecasting,ivs algorithm,input variable selection,artificial neural networks,ann model,water distribution system,ann development,chlorine disinfection,water quality modelling,water distribution system variable,non-linear ivs algorithm,chlorine residual forecasting,ivs procedure,model development,partial mutual information,variable selection,non linear dynamics,statistical model,artificial neural network,mutual information | Data mining,Heuristic,Feature selection,Computer science,Water quality modelling,Statistical model,Artificial intelligence,Mutual information,Artificial neural network,Machine learning,Water quality,Water supply | Journal |
Volume | Issue | ISSN |
23 | 10-11 | Environmental Modelling and Software |
Citations | PageRank | References |
23 | 1.22 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert J. May | 1 | 59 | 3.11 |
| Graeme C. Dandy | 2 | 441 | 47.01 |
| Holger R. Maier | 3 | 738 | 72.97 |
| John B. Nixon | 4 | 42 | 3.06 |