Flood Routing Calculation with ANN, SVM, GPR, and RTE Methods

Abstract

Flood routing analysis is of great importance in predicting floods and taking all necessary precautions in the area where the flood occurred. By applying different machine learning algorithms to historical flood data, the success of the established models in flood routing calculations has been measured. In this study, flood hydrograph data on 05.05.2014 was passed through the training and testing stages using the Support Vector Machine (SVM), Gaussian Process Regression (GPR), Regression Tree Ensembles (RTE), and Artificial Neural Networks (ANN) methods, then routing calculations were made by applying the flood data on 03.06.2015 to these models. The results were compared with either ANN, SVM, GPR, RTE, or measured values. Root Mean Square Errors (RMSE) and Correlation Coefficient (R) values were calculated at the end of the comparative analysis. In conclusion, it has been determined that verifying the flood routing results performed by the SVM is the best result.