River flow prediction of Hunza River by LSSVR, fuzzy genetic and M5 model tree using nearby station’s meteorological data

River runoff prediction plays a very vital role in water resources planning, hydropower designing and agricultural water management. In the current study, the prediction capability of three machine learning models, least square support vector regression (LSSVR), fuzzy genetic (FG) and M5 model tree (M5Tree), in modeling daily and monthly runoffs of Hunza River catchment (HRC) using own and nearby Gilgit climatic station data is examined. The prediction performances of three machine learning models are compared using three statistical indexes, namely, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Firstly, four previous time lagged values of runoff, rainfall and atmospheric temperature are used as inputs on basis of correlation analysis to validate and test the accuracy of three machine learning models. After analyzing the performance of various input combinations, optimal one is selected for each variable and then these optimal inputs are employed together to see the forecasting performance. In the first part of study, monthly runoff of HRC are predicted using inputs consisting of local previous monthly runoff values and monthly meteorological values of Gilgit station. The test results show that LSSVR provides more accurate prediction results than the other two machine learning models. In the second part, daily runoffs of HRC are predicted using own previous daily runoff and Gilgit station’s climatic values. In the test results, a better accuracy is obtained from LSSVR models in relative to the FG and M5Tree models. In the last part of study, daily runoffs of HRC are predicted using own runoff and climatic data of HRC. In the results, it is found that local climatic data slightly improved the all model’s prediction accuracy in comparison of other scenario which also uses nearby station’s climatic data. The LSSVR models again are found to be better than the FGA and M5Tree models. LSSVM generally performs superior to the FGA and M5Tree in forecasting daily stream flow of Hunza River using local stream flow and climatic inputs. Based on the results of study, LSSVR model is recommended for monthly and daily runoff prediction of HRC with or without local climatic data.