Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

中文翻译：

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内核极限学习机：使用天气数据估算每日露点温度的有效模型

准确估算露点温度 (Tdew) 在可持续水资源管理中具有至关重要的作用。本研究研究了核极限学习机 (KELM)、增强回归树 (BRT)、径向基函数神经网络 (RBFNN)、多层感知器神经网络 (MLPNN) 和多元自适应回归样条 (MARS) 模型，用于日常露点温度估计在美国的达勒姆和加州大学河滨站。每日时间尺度测量的水文气象数据，包括风速（WS）、最高气温（TMAX）、最低气温（TMIN）、最高相对湿度（RHMAX）、最低相对湿度（RHMIN）、蒸气压（VP）、土壤温度(ST)、太阳辐射 (SR) 和露点温度 (Tdew) 用于研究应用的预测模型。在均方根误差 (RMSE)、决定系数 (R2) 和 Nash-Sutcliffe 效率 (NSE) 统计指标方面，KELM 模型的结果与使用八种不同输入组合的其他模型进行了比较。结果表明，KELM 模型使用三个输入参数 VP、TMAX 和 RHMIN，在 Durham 站的 RMSE = 0.419 °C、NSE = 0.995 和 R2 = 0.995，以及七个输入参数 VP、ST、RHMAX、TMIN 、RHMIN、TMAX 和 WS，RMSE = 0.485 °C，NSE = 0.994，R2 = 0.994 在加州大学河滨站，在每日 Tdew 建模中表现出更好的性能。最后，通过比较结果得出的结论是，在应用的五个模型中，KELM 模型通过提高两个站点测试阶段的 BRT、RBFNN、MLPNN 和 MARS 模型的性能而被认为是最稳健的.