论文标题
在不同气候下使用深神经网络对每日参考蒸散的每日引用建模
Modelling of daily reference evapotranspiration using deep neural network in different climates
论文作者
论文摘要
精确且可靠的参考蒸散量（ET O）对于灌溉和水资源管理是必不可少的。 ET O由于其复杂的过程而难以预测。可以使用机器学习方法来解决这种复杂性。这项研究研究了人工神经网络（ANN）和深神经网络（DNN）模型的性能，以估计每日等。以前提出的ANN和DNN方法已经实现，并比较了它们的性能。六个输入数据，包括最高气温（T MAX），最低气温（T min），太阳辐射（R N），最大相对湿度（RH MAX），最小相对湿度（RH Min）和风速（U 2）（U 2）在1999年至2011年期间的4个气象站（Adana，Aksaray，Isparta和Niğde）来自4个气象站（Adana，Aksaray，Isparta和Niğde）。结果表明，与以前的ANN和DNN模型相比，我们提出的DNN模型可以达到每日ET O估计的令人满意的精度。在Aksaray中使用SELU激活函数（P-DNN-SELU）的DNN模型观察到了最佳性能，分别确定系数（R 2）为0.9934，均方根误差（RMSE）为0.2073，平均绝对误差（MAE）为0.1590。因此，可以建议使用P-DNN-SELU模型来估计世界其他气候区域的ET O。
Precise and reliable estimation of reference evapotranspiration (ET o ) is an essential for the irrigation and water resources management. ET o is difficult to predict due to its complex processes. This complexity can be solved using machine learning methods. This study investigates the performance of artificial neural network (ANN) and deep neural network (DNN) models for estimating daily ET o . Previously proposed ANN and DNN methods have been realized, and their performances have been compared. Six input data including maximum air temperature (T max ), minimum air temperature (T min ), solar radiation (R n ), maximum relative humidity (RH max ), minimum relative humidity (RH min ) and wind speed (U 2 ) are used from 4 meteorological stations (Adana, Aksaray, Isparta and Niğde) during 1999-2018 in Turkey. The results have shown that our proposed DNN models achieves satisfactory accuracy for daily ET o estimation compared to previous ANN and DNN models. The best performance has been observed with the proposed model of DNN with SeLU activation function (P-DNN-SeLU) in Aksaray with coefficient of determination (R 2 ) of 0.9934, root mean square error (RMSE) of 0.2073 and mean absolute error (MAE) of 0.1590, respectively. Therefore, the P-DNN-SeLU model could be recommended for estimation of ET o in other climate zones of the world.
