Evaluation of imputation techniques for infilling missing daily rainfall records on river basins in Ghana

ABSTRACT

Statistical imputation techniques were evaluated for infilling missing records in daily rainfall data within the Pra and Densu river basins in Ghana. The imputation techniques considered were mean, regression, multiple imputation by chain equation, k-nearest neighbour, probabilistic principal component analysis (PPCA), missForest, linear interpolation, hot deck, expectation maximization, Gaussian copula, inverse distance weighting and kriging. Different percentages of missing records (5%, 10%, 20% and 30%) were artificially introduced into the complete datasets. Then, the missing records were imputed and compared with the observed values. The root mean square error, mean absolute error, bias, coefficient of determination, similarity index and Kolmogorov-Smirnov performance statistics were used to evaluate the methods. The results were mixed depending on the performance metric used. However, the best candidates were regression, PPCA and missForest imputation techniques. These methods were better for estimating the numbers of dry and wet periods and the moderate to extreme rainfall values.

KEYWORDS:

• imputation
• missing data
• daily rainfall
• performance statistics
• Pra
• Densu

Editor A. Fiori Associate Editor D. Kingston

Editor A. Fiori Associate Editor D. Kingston

Acknowledgements

The authors acknowledge funding from Ministry of Foreign Affairs of Denmark/DANIDA Fellowship Centre through the “Building Climate-Resilience into Basin Water Management” (CREAM) research project (18-13-GHA). The authors are grateful to the Ghana Meteorological Agency for providing the rainfall data used in the study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Foreign Affairs of Denmark/DANIDA Fellowship Centre through the CREAM “Building Climate-Resilience into Basin Water Management” research project [18-13-GHA].