Advances in Computational Design

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Water consumption prediction based on machine learning methods and public data

  • Kesornsit, Witwisit (Government Data Solution Division, Department of Data Solution, Digital Government Development Agency (Public Organization)) ;
  • Sirisathitkul, Yaowarat (Department of Computer Engineering and Electronics, School of Engineering and Technology, Walailak University)
  • Received : 2021.09.16
  • Accepted : 2021.12.26
  • Published : 2022.04.25
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Abstract

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

Keywords

Acknowledgement

The data used in this study was kindly provided by Department of Data Solution, Open Government Data of Thailand, National Statistical Office of Thailand and Thai Meteorological Department. The authors would like to thank the government data solution project team for their collaborative support and guidance during this study. This research was financially supported by the new strategic research project (P2P), Walailak University, Thailand.

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