Wavelet-ANFIS models for forecasting monsoon flows: Case study for the Gandak River (India)
作者： Rajeev Ranjan Sahay , Vinit Sehgal
DOI: 10.1134/S0097807814050108
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摘要: WANFIS, a conjunction model of discreet wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS) was developed for forecasting the current-day flow in river when only available data are historical flows. Discreet decomposed observed time series (OFTS) into components which captured useful information on three resolution levels. A smoothened (SFTS) formed by filtering out noise recombining effective components. WANFIS is essentially an ANFIS with SFTS hydrograph as input, while autoregression (AR) models, comparison purpose, use OFTS input. For performance evaluation, models were utilized predicting daily monsoon flows Gandak River Bihar state India. During (June–October), this carries large making entire North unsafe habitation or cultivation. Based various indices, it concluded that simulate more reliably than AR models. The best performing model, four previous days’ predicted 80.7% accuracy 71.8 51.2% accuracies.
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