Abstract Computation of reference evapotranspiration (ETo) poses a challenge under limited meteorological data availability. However, even in this case, hourly data may be available since low-cost sensors can report hourly measurements. This study evaluates, for the first time, in regional and local scenarios, the use of limited hourly meteorological data (temperature and relative humidity or only temperature) to estimate daily ETo directly and by summing hourly ETo values, employing RF, XGBoost, ANN and CNN. The following options were evaluated: (i) use of daily input data (conventional approach); (ii) use of hourly data measured during a 24 h period + hourly extraterrestrial radiation (Ra) to estimate daily ETo directly; (iii) the same configuration of the last option, but with daily Ra instead of hourly Ra; and (iv) use of hourly data to estimate hourly ETo and then to estimate daily ETo by summing hourly ETo. All options used Ra. To develop and evaluate the models, two daily ETo targets were considered: ETod (computed using the daily version of the ASCE-PM equation) and ETosoh (computed by summing hourly ETo obtained with the ASCE-PM equation). Data from 53 weather stations located in the state of Minas Gerais, Brazil, were used. For all models, the best results were found using hourly data to estimate daily ETo directly. CNN models developed with 24 h hourly data + hourly Ra offered the best performance in all cases. In relation to the best models developed with daily data, RMSE reduced by up to 28.2 % (0.71 to 0.51) and NSE and R2 increased by up to 21.7 (0.69 to 0.84) and 11.4 % (0.79 to 0.88), respectively, in regional scenario. In local scenario, RMSE reduced by up to 22.4 % (0.58 to 0.45) and NSE and R2 increased by up to 10.1 (0.79 to 0.87) and 11.3 % (0.80 to 0.89), respectively.

中文翻译：

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使用机器学习和深度学习根据每小时温度和相对湿度估算每日参考蒸发量的新方法

摘要 在气象数据有限的情况下，参考蒸散量 (ETo) 的计算提出了挑战。然而，即使在这种情况下，每小时数据也可能可用，因为低成本传感器可以报告每小时测量值。本研究首次在区域和地方情景中评估使用有限的每小时气象数据（温度和相对湿度或仅温度）直接估算每日 ETo 并通过汇总每小时 ETo 值，采用 RF、XGBoost、ANN 和美国有线电视新闻网。评估了以下选项： (i) 使用每日输入数据（传统方法）；(ii) 使用在 24 小时期间测量的每小时数据 + 每小时外星辐射 (Ra) 来直接估算每日 ETo；(iii) 与最后一个选项相同的配置，但使用每日 Ra 而不是每小时 Ra；(iv) 使用每小时数据估算每小时 ETo，然后通过汇总每小时 ETo 来估算每日 ETo。所有选项都使用 Ra。为了开发和评估模型，考虑了两个每日 ETo 目标：ETod（使用 ASCE-PM 方程的每日版本计算）和 ETosoh（通过对使用 ASCE-PM 方程获得的每小时 ETo 求和计算）。使用了来自巴西米纳斯吉拉斯州 53 个气象站的数据。对于所有模型，最好的结果是使用每小时数据直接估计每日 ETo。使用 24 小时每小时数据 + 每小时 Ra 开发的 CNN 模型在所有情况下都提供了最佳性能。与使用每日数据开发的最佳模型相比，区域性的 RMSE 降低了 28.2%（0.71 到 0.51），NSE 和 R2 分别增加了 21.7（0.69 到 0.84）和 11.4%（0.79 到 0.88）设想。