Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms

Abstract

Support Vector Regression (SVR) combined with Invasive Weeds Optimization (IWO), standalone SVR, and Radial Basis Function Neural Networks are applied to estimate channel sinuosity in perennial rivers. With this aim, a dataset with 132 sinuosity data and related geomorphologic data, corresponding to 119 perennial streams, is considered. Bayesian Mutual Information theory is used to determine the parameters affecting channel sinuosity to reveal that bankfull depth affects sinuosity the most. Seven input parameter combinations for sinuosity prediction are considered, and in both training and testing stages, the SVR-IWO model $$\left( {R_{Train} = 0.959,RMSE_{Train} = 0.072, MAE_{Train} = 0.037, R_{test} = 0.892, RMSE_{Test} = 0.103, MAE_{Test} = 0.065} \right)$$ R Train = 0.959 , R M S E Train = 0.072 , M A E Train = 0.037 , R test = 0.892 , R M S E Test = 0.103 , M A E Test = 0.065 shows the best prediction performance while the standalone SVR model generated the results with performances of $$\left( {R_{Train} = 0.792,RMSE_{Train} = 0.158, MAE_{Train} = 0.141, R_{test} = 0.704, RMSE_{Test} = 0.163, MAE_{Test} = 0.151} \right)$$ R Train = 0.792 , R M S E Train = 0.158 , M A E Train = 0.141 , R test = 0.704 , R M S E Test = 0.163 , M A E Test = 0.151 . Model prediction uncertainty is quantified in terms of entropy for the three models considered, further confirming that the sinuosity set predicted by the SVR-IWO model is the closest to the observed set.