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| Prediction model based on influencing factors of water consumption |
| Wang Pu1,2, Wang Yizhi1,2, Zhang Jin3, Wang Ying1,2 |
| 1.Key Laboratory of Three Gorges Reservoir Region′s Eco-Environment, Ministry of Education, Chongqing University, Chongqing 400045, China; 2.National Centre for International Research of Low-carbon and Green Buildings, Chongqing University, Chongqing 400045, China; 3. Institute of Urban Water Management, Technische Universität Dresden, Dresden 01062, Germany |
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Abstract Urban water consumption prediction is full of complexity due to the different influencing factors and the uncertainty of the statistics database. A comprehensive water consumption prediction model was developed to slove this problem. Multiple approaches were integrated into this model. Specifically, the significant influencing factors of water consumption were selected by principal component analysis: then, the selected influencing factors were further classified by gray cluster analysis and gray relational analysis. Based on the evaluation of the significant influencing factors, a multilayer perceptron network was established by SPSS software, GRNN and BP neural networks were established by Matlab software. IOWA operator was also applied to the water prediction model. Consequently, a MLP-GRNN-BP comprehensive water demand consumption prediction model was developed based on the IOWA operator. The evaluation system,using the sum of squares error(SSE), the mean square error(MSE), mean absolute error(MAE), mean absolute percentage error(MAPE), mean square percentage error(MSPE)and Theil coefficient(μ), was established to evaluate the performance of the predictive models. The proposed models were applied to the city of Chongqing for the municipal water consumption prediction to verify the feasibility of these methods. The results show that the number of significant influencing factors for water consumption in Chongqing city can be reduced from 31 to 12, and the 12 significant influencing factors can be further classified into four clusters, and four kinds of the influencing factors can be analyzed and ranked respectively. MAPE, MSPE and Theil coefficient values are within 5%, when the BP, MLP, GRNN and MLP-GRNN-BP model are used in the case study, indication a good prediction.
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Received: 23 June 2014
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