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Assessment of different methods for estimation of missing data in precipitation studies

机译：评估降水研究中估计缺失数据的不同方法

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摘要

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.
机译：数据分析的结果取决于数据的质量和完整性。本文考虑了各种用于填充缺失降水数据的技术。为了评估填写缺失数据的不同方法的适用性，考虑了在六个不同站点收集的月降水数据。全套（无缺失值）用于预测月降水量。算术平均法，多元线性回归法和非线性迭代偏最小二乘算法效果最好。多元回归方法成功地估算了缺失的降水数据，这得到了文献中发表的结果的支持。对于来自五个相关站点的降水数据，多重插补方法产生了最准确的结果。决策树算法是显式的，因此在需要深入了解决策制定时使用。提出了全面的错误分析。

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