Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models

Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988-2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). The results demonstrate that the BA-Kstar ensemble model achieved superior performance (R2 = 0.91, RMSE = 1.60, NSE = 0.91, and RSR = 0.30). Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. This study underscores the reliability and efficacy of hybrid ML models for Ep forecasting, with significant implications for their broader application in diverse climates and geographical regions.