Artificial neural network analysis for reliability prediction of regional runoff utilization

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

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)315-326
Number of pages12
JournalEnvironmental Monitoring and Assessment
Issue number1-4
Publication statusPublished - 2010 Feb 1

All Science Journal Classification (ASJC) codes

  • General Environmental Science
  • Pollution
  • Management, Monitoring, Policy and Law


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