Comparative Analysis of Rainfall Data Estimation Methods: A Case Study of Tanggamus Regency

Abstract

Rainfall data is vital for water resource systems and natural disaster management. It is required to have complete data to know the needs and structural requirements. However, in reality, plenty of cases of missing or broken rainfall data happened due to specific circumstances. To estimate missing rainfall data, several methods can be used, such as the Arithmetic Mean Method, Normal Ratio Method, Inversed Square Distance Method, and Modified Method. This study analyzed and compared Pearson's Correlation Coefficient and Root Mean Square Error (RMSE) among the methods. The result indicates that based on Pearson's Correlation Coefficient, the Modified Inversed Square Distance Method tends to be the best method for estimating missing rainfall data in the Tanggamus Regency. Several factors contribute to Pearson's Correlation Coefficient, such as rainfall data consistency and the elevation of stations used. Otherwise, based on the Mean Square Error, the Modified Arithmetic Mean Method is the best to use. This research also indicated that Pearson's Correlation Coefficient is better for evaluating missing data than RMSE.