Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models
作者： Vinit Sehgal , Rajeev Ranjan Sahay , Chandranath Chatterjee
DOI: 10.1007/S11269-014-0584-4
关键词:
摘要: Wavelet based flood forecasting models are known to perform better than conven- tional models, yet the effect of way wavelet components combined develop a model on performance, is inadequately investigated. To demonstrate this, two types wavelet- adaptive neuro-fuzzy inference system (WANFIS), i.e. WANFIS-split data (WANFIS-SD) and WANFIS-modified time series (WANFIS-MS) developed forecast river water levels with 1-day lead time. these first original level (OLTS) decomposed into discrete (DWCs) by transform (DWT) upto three resolution levels. In WANFIS-SD, all compo- nents used as inputs while WANFIS-MS ignores noise utilizes only effective components. The effectiveness evaluated through application Indian rivers, Kamla Kosi, which vary significantly in their catchment area flow patterns. proposed found accurately. On comparison, WANFIS-SD for high
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