PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS
Keywords: Flood, Data-Driven Methods, Annual Maximum Streamflow, Precipitation, Modelling, Karkheh Basin
Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R2 and RMSE of 85.89% and 30.02 m3/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models.