Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R² value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R² value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.

中文翻译：

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基于机器学习方法的大河流域陆地蒸散综合指数（CTEI）预测

干旱是全球气候格局的基本物理特征。在过去的几十年中，自然灾害加剧了自然灾害的发生，严重影响了农业系统，经济，环境，水资源和供应。因此，至关重要的是开发新技术，以便能够以令人满意的时空分辨率对大面积的干旱进行综合测定和观测。这项研究模拟了在恒河流域开发的一种新的干旱指数，称为陆地陆地蒸散综合指数（CTEI）。为此，应用了五种源自人工智能理论的机器学习（ML）技术：支持向量机（SVM）算法，决策树，Matern 5/2高斯过程回归，增强树和袋装树。这些技术由十二种不同的模型驱动，这些模型是根据卫星数据和水文气象参数的输入组合生成的。结果表明，第八个模型表现最好，并且在所有模型中均优于后者，而SVM算法导致R^2的值为0.82，最低误差以均方根误差（RMSE）（0.33）和平均绝对误差（MAE）（0.20）表示，其后是R ²值为0.75的Matern 5/2高斯模型，以及RMSE和MAE分别为0.39和0.21 mm /天。此外，在这五种方法中，SVM和Matern 5/2高斯方法是我们在恒河盆地CTEI预测研究中表现最好的ML算法。