Artificial neural network analysis for reliability prediction of regional runoff utilization

S. C. Lee, Hsien-Te Lin, T. Y. Yang

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)

摘要

Many factors in the reliability analysis of planning the regional rainwater utilization tank capacity need to be considered. Based on the historical daily rainfall data, the following four analyzing procedures will be conducted: the regional daily rainfall frequency, the amount of runoff, the water continuity, and the reliability. Thereafter, the suggested designed storage capacity can be obtained according to the conditions with the demand and supply reliability. By using the output data, two different types of artificial neural network models are used to build up small area rainfall-runoff supply systems for the simulation of reliability and the prediction model. They are also used for the testing of stability and learning speed assessment. Based on the result of this research, the radial basis function neural network (RBFNN) model, using the Gaussian function that has a similar trend as the nature as basic function, has better stability than using the back-propagation neural network (BPNN) model. Despite the fact that RBFNN was more reliable than BPNN, it still made a conservative estimate for the actual monitoring data. The error rate of RBFNN was still higher than the correction of BPNN 4-3-1-1. This should have significant benefit in the future application of the instantaneous prediction or the development of related intelligent instantaneous control equipment.

原文English
頁(從 - 到)315-326
頁數12
期刊Environmental Monitoring and Assessment
161
發行號1-4
DOIs
出版狀態Published - 2010 2月 1

All Science Journal Classification (ASJC) codes

  • 環境科學 (全部)
  • 污染
  • 管理、監督、政策法律

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