The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.

中文翻译：

---

基于机器学习的二次降水估算合并：印度克里希纳河流域的开发和评估

该研究提出了使用机器学习（SPEM2L）算法的二次降水估计合并，用于合并多个全球降水数据集，以改善时空降雨特征。SPEM2L在1985年至2018年之间的34年中一直应用于印度克里希纳河流域（KRB），使用来自三个二次沉淀产品（SPPs）的每日测量值。在四个组合下的三个SPP上应用了十六种机器学习算法（MLA），以集成和测试MLA的性能，以准确表示降雨模式。根据印度气象局提供的基于量规的网格数据集对单个SPP和集成产品进行了验证。通过采用连续和分类统计，在不同的时间尺度和不同的气候带进行了验证。采用三个SPP集成的具有贝叶斯正则化（NBR）算法的多层感知器神经网络的性能优于所有其他机器学习模型（MLM）和两个数据集集成组合。合并后的NBR产品在所有时间范围以及所有气候区的连续和分类统计方面均表现出改进。我们的结果表明，SPEM2L程序可以成功地用于任何具有较差的测量网络或单一降水产品性能无效的区域或盆地。采用三个SPP集成的具有贝叶斯正则化（NBR）算法的多层感知器神经网络的性能优于所有其他机器学习模型（MLM）和两个数据集集成组合。合并后的NBR产品在所有时间范围以及所有气候区域的连续和分类统计方面均表现出改进。我们的结果表明，SPEM2L程序可以成功地用于任何具有较差的测量网络或单一降水产品性能无效的区域或盆地。采用三个SPP集成的具有贝叶斯正则化（NBR）算法的多层感知器神经网络的性能优于所有其他机器学习模型（MLM）和两个数据集集成组合。合并后的NBR产品在所有时间范围以及所有气候区的连续和分类统计方面均表现出改进。我们的结果表明，SPEM2L程序可以成功地用于任何具有较差的测量网络或单一降水产品性能无效的区域或盆地。