In this study, two kernel-based models were used which include Support Vector Regression (SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based models that are M5 and Random Forest (RF) for estimating missing monthly precipitation data in Antakya, Dortyol, Iskenderun and Samandag stations, which are the important precipitation stations in the Eastern Mediterranean region, Turkey. For this purpose, firstly 10% random precipitation data were assumed as missing data for the period 1980-2019. Secondly, the missing data in each station was estimated with the data of other stations within the framework of four data combinations scenarios. In Kernel-based SVR and GPR methods, the RBF kernel gave suitable results for the selected study area. While SVR and RF methods gave very close estimation results, the SVR method gave relatively better results than the other methods especially in error minimizing aspects. Gaussian function based GPR model generally tries to estimate missing data closer to means. This is the main disadvantage of the GPR model and therefore it is unsuccessful in the estimation process. Finally, the results showed that the algorithms based on machine learning are successful in estimating the missing precipitation data.

在这项研究中，使用了两个基于核的模型，包括支持向量回归（SVR）和高斯过程回归（GPR），并与两个基于树的模型（M5和随机森林（RF））进行了比较，以估计缺失的月降水量数据土耳其东部地中海地区重要的降水站是安塔基亚，多尔蒂奥尔，伊斯肯德伦和萨曼达格站。为此，首先假定10％的随机降水数据为1980-2019年期间的缺失数据。其次，在四个数据组合方案的框架内，与其他站点的数据一起估算每个站点的丢失数据。在基于内核的SVR和GPR方法中，RBF内核为选定的研究区域提供了合适的结果。尽管SVR和RF方法得出的估算结果非常接近，SVR方法比其他方法具有相对更好的结果，尤其是在最小化错误方面。基于高斯函数的GPR模型通常试图估计更接近均值的缺失数据。这是GPR模型的主要缺点，因此在估算过程中不成功。最后，结果表明，基于机器学习的算法可以成功地估计缺失的降水数据。