Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load

Abstract

This study is aimed on successful modeling of Ajichay River Suspended Sediment Load (SSL) which is significant object in watershed planning and management. Therefore, a two-stage modeling strategy was proposed in order to handle spatio-temporal variation of SSL. At temporal stage, Support Vector Machine (SVM) was utilized for three stations located on the Ajichay River to find the non-linear relationship of SSL in time domain. Different input sets were examined for the SVM via sensitivity analysis. Results of temporal modeling stage were used in spatial modeling. In spatial modeling stage, firstly semi-variogram of monthly SSL data was calculated and then theoretical semi-variogram model was fitted to the empirical variogram. It was found that Gaussian model is the best fitted model for the study case. The obtained results of semi-variogram were imported into Geostatistic tool for spatial estimation of SSL in sites where there is not any measurement. Results of temporal modeling stage demonstrated that input data as combination of SSL and discharges at 1 month and 12 monthes ago employing RBF based SVM could lead to the best performance for each station. Spatial modeling performance was improved relatively using streamflow dataset. The obtained results show that the hybrid of SVM and Spatial statistics methods could predict and simulated SSL appropriately by enjoying unique features of both approaches.