High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models
High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models
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干旱埃及的高分辨率参考蒸散量：经验模型和人工智能模型的比较分析和评估
DOI：
10.1002/joc.7894
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
N. Amer
中科院分区：
文献类型：
--
作者：
Mohamed Tarek Sobh;Mohamed Salem Nashwan;N. Amer
准确估算蒸发蒸腾量在埃及这样的干旱地区至关重要，因为这些地区降水稀少，水资源短缺。这项研究调查了31个广泛使用的经验方程和20个模型的性能，这些模型使用五种人工智能（AI）算法来估计参考蒸散量（ET 0），以生成埃及的网格化高分辨率每日ET 0估计值。人工智能算法包括支持向量机-径向基函数（SVM-RBF），随机森林（RF），数据处理神经网络的分组方法（GMDH-NN），多元自适应回归样条（MARS）和动态进化神经模糊干扰系统（DENFIS）。利用分布在埃及的41个台站的日观测记录，采用FAO 56 Penman-Monteith方程作为参考估计，计算ET 0。使用多参数Kling-Gupta效率（KGE）指标作为其在单个值中表示不同统计误差/一致性特征的稳健性的评价指标。按类别划分，基于辐射的经验方程在复制FAO 56-PM时表现更好，其次是基于温度和传质的经验方程。根据站点排序，发现里奇方程在埃及（中位数KGE 0.76）的总体效果最好，其次是Caprio（中位数KGE 0.64）和Penman（中位数KGE 0.52）方程。另一方面，以最高和最低温度、风速和相对湿度作为预测因子的RF模型优于其他AI算法。总体而言，RF模型在所有AI模型和经验方程中表现最好。生成的0.10° × 0.10°每日ET 0估计值使我们能够使用修正的Mann-Kendall检验和Sen斜率估计器检测到依赖农业的尼罗河三角洲显著增加0.12-0.16 mm·decade−1。
Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ET0) to generate gridded high‐resolution daily ET0 estimates over Egypt. The AI algorithms include support vector machine‐radial basis function (SVM‐RBF), random forest (RF), group method of data handling neural network (GMDH‐NN), multivariate adaptive regression splines (MARS), and dynamic evolving neural fuzzy interference system (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ET0 using FAO56 Penman–Monteith equation as a reference estimate. The multiparameter Kling‐Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replicating FAO56‐PM followed by temperature‐ and mass‐transfer‐based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.76) followed by Caprio (median KGE 0.64), and Penman (median KGE 0.52) equations based on station‐wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. Overall, the RF model performed the best among all the AI models and empirical equations. The generated 0.10° × 0.10° daily estimates of ET0 enabled the detection of a significant increase of 0.12–0.16 mm·decade−1 in the agricultural‐dependent Nile Delta using the modified Mann–Kendall test and Sen's slope estimator.
影响因子：
14.6
作者：
Martijn Gough
通讯作者：
Martijn Gough