Evaluation of wavelet – least square support vector machine hybrid model to rainfall time series spatiotemporal disaggregation

Subject Areas : Farm water management with the aim of improving irrigation management indicators

nima farboudfam ¹ ( Department of Water Resources Engineering, Engineering faculty, Roudehen Branch, Islamic Azad University, Roudehen, Iran. )
Vahid Nourani ² ( Department of Water Resources Engineering, Civil engineering faculty, Tabriz university, Tabriz, Iran Department of Water Resources Engineering, Engineering faculty, Roudehen Branch, Islamic Azad University, Roudehen, Iran. )
babak aminnejad ³ ( Department of Water Resources Engineering, Engineering faculty, Roudehen Branch, Islamic Azad University, Roudehen, Iran. )

Received: 2018-01-12 Accepted : 2019-02-24 Published : 2019-04-21

Keywords: Wavelet Transform, Disaggregation, Hybrid Model, Rainfall Time series, Least Square Support Vector Machine,

Abstract :

The need to simulate rainfall time series at different scales for engineering purposes on the one hand and 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. In this study, for disaggregating the Tabriz and Sahand rain gauges time series, according to nonlinear characteristics of time scales, wavelet- Least Square Support Vector Machine (WLSSVM) hybrid model is proposed and daily data of four rain gauges and monthly data of six rain gauges from Urmia Lake Basin for ten years were decomposed with wavelet transform and then by using mutual information and correlation coefficient criteria, the subseries were ranked and superior subseries were used as input data of Least Square Support Vector Machine (LSSVM) model for disaggregating the Tabriz and Sahand rain gauges monthly rainfall time series to the daily time series. Results obtained from the WLSSVM disaggregation model were compared with the results of LSSVM and traditional multiple linear regression models. The results of WLSSVM model to LSSVM and multiple linear regression models at validation stage in the optimized case for Tabriz rain gauge were increased 10% and 37.5% and in the optimized case for Sahand rain gauge were increased 24.5% and 46.7% respectively. It was concluded that hybrid WLSSVM model has a higher accuracy than two other methods and can be considered as an accurate disaggregation model to disaggregate the rainfall time series.