LAUSR

Flood prediction is very critical for efficient use of flood control reservoirs, and earthen and concrete levees systems. As a result, flood prediction has a great importance in catchment areas. In this study, rainfall and air temperature were predicted in Karun-4 basin in southwest of Iran by using three different models including WRF numerical model, ANN, and SVM model in order to evaluate accuracy in flood forecasting. The rainfall and air temperature prediction and flood forecasting results using different schemas of WRF model indicated that MYJLG schema has more accuracy than other schemas. Partial mutual information (PMI) algorithm was used in order to determine the effective input variables in ANN and SVM models. The results of using PMI algorithm showed that rainfall at rain gauge stations in the next 6 hrs indicated that the effective variables included relative humidity, current rain status (present rainfall), rainfall in 6 hrs ago, and rainfall and temperature of 12 hrs ago. Also, the PMI algorithm results for predicting air temperature in the next 6 hrs showed that the effective input variables including the temperature of 18 hrs ago, current temperature, temperature of 12 hrs ago, and temperature of 6 hrs ago. The comparison between the peak discharge and runoff height values of the predicted flood hydrograph in different models showed that SVM model had more efficiency and accuracy than the other two models in predicting rainfall, air temperature, and flood hydrograph.