Research on BP Neural Network Model for Water Demand Forecasting and its Application

Abstract:

It is difficult to determine a proper neurons number of the mid-layer when using the BP neural network for water demand forecasting. Aiming at the problem, the BP neural network is presented in this paper for water demand forecasting. A suitable neurons number in the mid-layer is calculated based on the empirical formula method and trial and error method. A certain basin in China is taken as a case study. The results indicate that the mean relative error is 2.42%. The water consumption is 42.8 billion m3 in 2015 and 43.6 billion m3 in 2030 in the study area. The results are useful for water resources planning and management.

• Periodical: Applied Mechanics and Materials (Volumes 170-173)
• Pages: 2352-2355
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.170-173.2352
• Online since: May 2012
• Authors: Yue Feng Sun*, Hao Tian Chang, Zheng Jian Miao
• Keywords: Basin, BP Neural Network (BPNN), Water Demand Forecasting, Water Resource Management

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