Estimating The Monthly Pan Evaporation With Limited Climatic Data In Dryland Based On The Extended Long Short-term Memory Model Enhanced With Meta-heuristic Algorithms

This study proposed a novel hybrid model to estimate the monthly pan evaporation (Ep) in dryland by integrating long short-term memory (LSTM) with grey wolf optimizer (GWO) algorithm and Kendall-τ correlation coefficient. The model performance was compared to the performance of other methods based on the evaluation metrics, including root mean squared error (RMSE), the normalized mean squared error (NMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and Nash-Sutcliffe coefficient of efficiency (NSCE). The results indicated that the hybrid Kendall-τ-GWO-LSTM model exhibits better model performance than the other hybrid models. Thus, the hybrid Kendall-τ-GWO-LSTM model was highly recommended for estimating pan Ep with limited meteorological information in dryland.