Journal article
Assessment of different methods for estimation of missing data in precipitation studies
Nordic hydrology, Vol.48(4), pp.1032-1044
08/01/2017
DOI: 10.2166/nh.2016.364
Abstract
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.
Details
- Title: Subtitle
- Assessment of different methods for estimation of missing data in precipitation studies
- Creators
- Mohammad-Taghi Sattari - University of TabrizAli Rezazadeh-Joudi - Young Researchers and Elite Club, Maragheh Branch, Islamic Azad University, Maragheh, IranAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Nordic hydrology, Vol.48(4), pp.1032-1044
- DOI
- 10.2166/nh.2016.364
- ISSN
- 0029-1277
- eISSN
- 2224-7955
- Language
- English
- Date published
- 08/01/2017
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984187058102771
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