Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models

Abstract

Accurate predicting of lake level fluctuations is essential and basic in water resources management for water supply purposes. The predicting of lake level is complicated because of it is affected by nonlinear hydrological processes. This paper applies integrated wavelet and auto regressive moving average (ARMA), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) models for forecasting monthly lake level fluctuations. First, lake level time series is decomposed into low and high frequency components by using discrete wavelet transform. Then, each component is separately predicted by using ARMA, ANFIS and SVR models. Finally, the predicted components are summed to obtain estimated original lake level time series. The performance of the proposed WSVR (Wavelet-SVR), WANFIS (Wavelet-ANFIS) and WARMA (Wavelet-ARMA) models is compared with single ARMA, SVR and ANFIS models. Results show that the integrated models give better precision in forecasting lake levels in the study region when compared to single models. WSVR model is found to be slightly better than the other integrated models.