The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EP_m) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.

评估了包括多模型人工神经网络（MM-ANN），多元自适应回归样条（MARS），支持向量机（SVM），多基因遗传规划（MGGP）和'M5Tree'在内的几种预测模型的潜力模拟月度蒸发量（EP _m）在印度的两个车站（例如Ranichauri和Pantnagar）。使用每月的气候信息来模拟锅蒸发。使用Gamma检验（GT）确定MM-ANN，MGGP，MARS，SVM和M5Tree的最大有效输入变量。使用几种统计标准（例如，平均绝对百分比误差（MAPE），威尔莫特一致性指数（WI），均方根误差（RMSE），纳什-苏特克利夫效率（NSE）以及Legate和McCabe's索引（LM））和外观检查。结果显示，MM-ANN-1和MGGP-1模型（NSE，WI，LM，RMSE，MAPE为0.954、0.988、0.801、0.536毫米/月，在Pantnagar站为9.988％，以及0.911、0.975、0.724，和0.364毫米/月，在Ranichauri站为12.297％，在测试期间，输入变量等于6的输入变量分别比其他技术更成功，以模拟Ranichauri和Pantnagar站的月度锅蒸发。因此，拟议的MM-ANN-1和MGGP-1模型的结果将对水资源管理方面的当地利益相关者有所帮助。