Missing rainfall data estimation—an approach to investigate different methods: case study of Baghdad

Abstract

Abstract The missing of the meteorological data in Iraq is common due to malfunction of measuring devices, security status, and human effects. The study tested 17 missing precipitation data estimation methods in Baghdad city as a case study, where, all the surrounding stations around Baghdad experienced the missing of data for various reasons, and some of the missing data are for a full year record. The methods examined in this study are based on different approaches, some of the methods are based upon the distances to the targeted station, others are upon regression factors, and there are also methods that combine several factors. There are also other types of missing data filling methods which depend on imputation and artificial intelligence. The investigation of the most accurate method to find the missing data will assist researchers and decision makers to fill the gap in their analysis in one of the most vulnerable countries in terms of drought and climate changes impacts. Results showed that Expectation Maximization (EM) method utilization has the best results with the least errors, and Multiple Linear Regression (MLR) method was ranked the second best method. In general, all of the applied methods had resulted acceptable interpolations, and it was clear that the combined methods have low significance on the results in comparison with others. All of these findings are limited to the study area meteorological and spatial conditions.