Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization...

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam. Several hydrological and meteorological variables, including inflow and outflow of the reservoir, lake area behind the dam, temperature, overflow from the reservoir, precipitation, and evaporation at the previous month, were considered input data to predict the evaporation at the current month. Monthly data from October 1999 to September 2020 were used during the modeling. First, the single adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector regression (LS-SVR) models were evaluated for predicting the amount of evaporation using different scenarios defined based on the different combinations of input variables. The results showed that LS-SVR with RMSE = 2.77, MAPE = 2.48, and NSE = 0.93 provided a better prediction than ANFIS. Second, the Harris hawks optimization (HHO) algorithm was used to optimize the parameters of ANFIS to check for the possibility of performance improvement. The hybrid ANFIS-HHO model predicted the evaporation with RMSE = 2.35, MAPE = 1.55, and NSE = 0.95, respectively. The Taylor's diagram also demonstrated the superior performance of the hybrid ANFIS-HHO model than the LS-SVR and ANFIS models. The best scenario for all three models included all input variables but the area behind the dam into the models. The methodology proposed in this study is useful for predicting the evaporation from dam reservoirs under the influence of various dam variables.