Rainfall time series disaggregation in mountainous regions using hybrid wavelet-artificial intelligence methods

In mountainous regions, rainfall can be extremely variable in space and time. The need to simulate rainfall time series at different scales on one hand and the lack of recording such parameters in small scales because of administrative and economic problems, on the other hand, disaggregation of rainfall time series to the desired scale is an essential topic for hydro-environmental studies of such mountainous regions. Hybrid models development by combining data-driven methods of least square support vector machine (LSSVM) and Artificial Neural Network (ANN) and wavelet decomposition for disaggregation of rainfall time series are the purpose of this paper. In this study, for disaggregating the Tabriz and Sahand rain-gauges time series, according to nonlinear characteristics of observed time scales, wavelet-least square support vector machine (WLSSVM) and wavelet-artificial neural network (WANN) hybrid models were proposed. For this purpose, daily data of four rain-gauges and monthly data of six rain-gauges from mountainous basin of the Urmia Lake for seventeen years were decomposed with wavelet transform and then using mutual information and correlation coefficient criteria, the sub-series were ranked and superior sub-series were used as input data of LSSVM and ANN models for disaggregating the monthly rainfall time series to the daily time series. Results obtained by these hybrid disaggregation models were compared with the results of LSSVM, ANN and classic multiple linear regression (MLR) models. The efficiency of WANN model with regard to the WLSSVM, ANN, LSSVM and MLR models at validation stage in the optimized case for Tabriz rain-gauge showed up to 9.1%, 22%, 20% and 50% increase and in the optimized case for Sahand rain-gauge showed up to 4.5%, 21.1%, 30.2% and 53.3% increase, respectively.