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

Abstract:

Article Preview

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.

Info:

Periodical:

Edited by:

Mingjin Chu, Xiangran Li, Jingzhou Lu, Xingmin Hou and Xiaogang Wang

Pages:

2352-2355

DOI:

10.4028/www.scientific.net/AMM.170-173.2352

Citation:

Y. F. Sun et al., "Research on BP Neural Network Model for Water Demand Forecasting and its Application", Applied Mechanics and Materials, Vols. 170-173, pp. 2352-2355, 2012

Online since:

May 2012

Export:

Price:

$35.00

* - Corresponding Author

[1] Nasseria M., Moeinib A. and Tabeshc M. Expert Systems with Applications Vol. 38(6) (2011), pp.7387-7395.

[2] Weng Wenbin, Wang Zhongjing and Zhao Jianshi. Modern water resources planning – theory, method, and technology. Tsinghua University Press, Beijing (2004) (In Chinese).

[3] Shaofeng Yuan, Jun Lu. Journal of Agricultural Mechanization Research Vol. 10 (2003) pp.5-8 (In Chinese).

[4] Rongfeng Li. Shanxi Hydrotechnics Vol. 10(4) (2003) pp.50-53 (In Chinese).

[5] Xiaoling Wang, Yuefeng Sun, Lingguang Song and Chuanshu Mei. Journal of Environmental Management Vol. 90(8) (2009), pp.2612-2619 (In Chinese).

[6] Wei Yang, Ende Wang, and Chang Chen. Volcanology & Mineral Resources Vol. 24(3) (2003), pp.217-221 (In Chinese).

[7] Tao Shang, Ning An, and Changde Wang. Journal of Central China Normal University Vol. 36(4) (2002), pp.456-458 (In Chinese).

[8] Yu S., Zhu K., and Diao F. Applied Mathematics and Computation Vol. 195(1) (2008), pp.66-75.

[9] Yu Wang, Qiyi Guo, and Weigang Li. Computer Automated Measurement & Control Vol. 13(1) (2005), pp.39-42 (In Chinese).

[10] Wenge Zhang, Zening Wu, and Hongbo Lu. Henan Science Vol. 21(2) (2003), pp.202-206 (In Chinese).

[11] Zhiqiang Lu, Shuquan Li, and Liangying Zhao, et al. Hebei Water Resources and Hydropower Engineering Vol. 1 (2005), pp.18-20 (In Chinese).

[12] Yu S., Zhu K., and Diao F. Applied Mathematics and Computation Vol. 195(1) (2008), pp.66-75.

[13] Funahashi K. Neural Networks Vol. 2 (1989), pp.183-192.

[14] Hsu K, Gupta H V and Sorooshian S. Water Resources Research Vol. 31(10) (1995), pp.2517-2530.

[15] Hecht-Nielsen R. Komogrov's mapping neural network existence theorem. Proceedings of the international conference on Neural Networks. New York, IEEE Press, (1987), pp.11-13.

In order to see related information, you need to Login.