Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model - wavelet transformation, data-driven models, and genetic algorithm (GA) - for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE=12.15m(3)/s, EC=87.32%, R=.934) and WANN (RMSE=15.73m(3)/s, EC=78.83%, R=.888) models improved the performances of ANFIS (RMSE=23.13m(3)/s, EC=54.11%, R=.748) and ANN (RMSE=22.43m(3)/s, EC=56.90%, R=.755) during the test period.