Application of Entropy Concept for Input Selection of Wavelet-ANN Based Rainfall-Runoff Modeling
作者: V. Nourani T. R. Khanghah A. H. Baghanam
刊名: Journal of Environmental Informatics, 2015, Vol.26 (1)
来源数据库: International Society for Environmental Information Science
DOI: 10.3808/jei.201500309
关键词: rainfall-runoff modelingFeed Forward Neural Networkwavelet transformfeature extractionShannon entropy (information content)multi-step-ahead forecasting
原始语种摘要: This paper presents a Wavelet-based Artificial Neural Network (WANN) approach to model rainfall-runoff process of the Delaney Creek and Payne Creek watersheds with distinct hydro-geomorphological characteristics, located in Florida. Wavelet is utilized to handle the multi-frequency characteristics of the process in daily and monthly time scales. Thus, rainfall and runoff time series were decomposed into several sub-series by various mother wavelets. Due to multiple components obtained through wavelet decomposition, input sets to the Feed Forward Neural Network (FFNN) were enhanced. The application of two information content based criteria (i.e., entropy, H, and mutual information, MI) to select more reliable input sets (among all potential input sets) and to have better insight into the...
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  • process 过程
  • information 报告
  • proposed 建议的
  • more 更多
  • reliable 确实的
  • ANN All Numeral Numbering
  • modeling 制祝型
  • uncertainty 不定
  • Application 应用
  • Creek 支流