In the present study, two innovative techniques namely, Deep Learning (DL) and Gradient boosting Machine (GBM) models are developed based on a maximum air temperature ‘univariate modeling scheme’ for modeling the monthly pan evaporation (E_pan) process. Monthly air temperature and pan evaporation are used to build the predictive models. These models are used for evaluating the evaporation prediction for the Kiashahr meteorological station located in the north of Iran and Ranichauri station positioned in Uttarakhand State of India. Findings indicated that the deep learning model was found best at Kiashahr station for testing datasets MAE (0.5691, mm/month), RMSE (0.7111, mm/month), NSE (0.7496), and IOA (0.9413). It can be concluded that in the semi-arid climate of Iran both of the used methods had the good capability in modeling of monthly E_pan. However, DL predicted monthly E_pan better than GBM. Moreover, the highest accuracy of the deep learning model was also observed for the Ranichauri station in terms of MAE = 0.3693 mm/month, RMSE = 0.4357 mm/month, NSE = 0.8344, & IOA = 0.9507 in testing stage. Overall, results expose the superior performance of DL-based models for both study stations and can also be utilized for various other environmental modeling.

在本研究中，基于最大气温“单变量建模方案”开发了两种创新技术，即深度学习 (DL) 和梯度提升机 (GBM) 模型，用于模拟月锅蒸发 ( E _pan） 过程。每月气温和锅蒸发量用于构建预测模型。这些模型用于评估位于伊朗北部的 Kiashahr 气象站和位于印度北阿坎德邦的 Ranichauri 站的蒸发预测。研究结果表明，深度学习模型在 Kiashahr 站测试数据集 MAE（0.5691，mm/月）、RMSE（0.7111，mm/月）、NSE（0.7496）和 IOA（0.9413）时表现最好。可以得出结论，在伊朗半干旱气候条件下，两种方法均具有良好的月E _pan模拟能力。然而，DL 预测每月E _pan比GBM好。此外，在测试阶段，Ranichauri 站的深度学习模型精度最高，MAE = 0.3693 mm/月，RMSE = 0.4357 mm/月，NSE = 0.8344，IOA = 0.9507。总体而言，结果揭示了两个研究站基于 DL 的模型的卓越性能，也可用于各种其他环境建模。