A Comparison of Different Methods for Rainfall Imputation: A Galician Case Study
Source abstract
Abstract
With the ultimate goal of developing models that involve the use of environmental variables, a GIS-based application is being developed that is circumscribed to the region of Galicia (Spain). Ten-minute data of six meteorological variables were collected from 150 stations of the MeteoGalicia network over a period of 18 years, but the time series data are not complete. In order to estimate missing rainfall data, four imputation methods were evaluated in this study: missForest, MICE, Amelia II, and inverse distance weighting (IDW). Crossvalidation results show that the precipitation is out of phase in the different stations due to their geographical locations, and the imputation can be improved with a displacement of the time series; on the other hand, the missForest method provided better results in the imputation of this meteorological variable than the MICE, Amelia, or IDW.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrology and Drought Analysis · Precipitation Measurement and Analysis · Climate variability and models
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departament of Computer Engineering, Facultad de Informática, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
Department of Industrial Engineering, Universidade da Coruña, Campus de Esteiro, 15403 Ferrol, Spain
Department of N.S. and M.E., Universidade da Coruña, Paseo de Ronda, 51, 15011 A Coruña, Spain
References (23)
Show source-provided references
1. Ministerio de Obras Públicas, and Transporte y Medio Ambiente (1987). Cálculo Hidrometeorológico de Caudales Máximos en Pequeñas Cuencas de Carreteras, Alanmer.
2. A mathematical framework for studying rainfall intensity-duration-frequency relationships10.1016/S0022-1694(98)00097-3 ↗
3. Interpolation of missing precipitation records10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2 ↗
4. A three-way model for interpolating for monthly precipitation values10.1175/1520-0493(1992)120<2561:ATWMFI>2.0.CO;2 ↗
5. Comparison of neural network methods for infilling missing daily weather records10.1016/j.jhydrol.2007.04.020 ↗
6. A methodoloy for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy)
7. Helmi, A.M., Elgamal, M., Farouk, M.I., Abdelhamed, M.S., and Essawy, B.T. (2023). Evaluation of Geospatial Interpolation Techniques for Enhancing Spatiotemporal Rainfall Distribution and Filling Data Gaps in Asir Region, Saudi Arabia. Sustainability, 15.10.3390/su151814028 ↗
8. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy
9. Creating a serially complete, national daily time series of temperature and precipitation for the western United States10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2 ↗
10. CUTOFF: A spatio-temporal imputation method10.1016/j.jhydrol.2014.11.012 ↗
11. Spatial-temporal rainfall simulation using generalized linear models10.1029/2004WR003739 ↗
12. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys, John Wiley.10.1002/9780470316696 ↗
13. Missing data: Our view of the state of the art10.1037/1082-989X.7.2.147 ↗
14. Wayman, J.C. (2003, January 21–25). Multiple Imputation For Missing Data: What Is It and How Can I Use It?. Proceedings of the Annual Meeting of the American Educational Research Association, Chicago, IL, USA.
15. Yang, Y. (2005). Multiple Imputation for Missing Data: Concepts and New Development, SAS Institute Inc.
16. Multiple imputation of discrete and continuous data by fully conditional specification10.1177/0962280206074463 ↗
17. Missforest-non-parametric missing value imputation for mixed-type data10.1093/bioinformatics/btr597 ↗
18. Random forest10.1023/A:1010933404324 ↗
19. van Buuren, S., and Oudshoorn, K. (1999). Flexible Multivariate Imputation by MICE, TNO Prevention and Health.
20. Amelia II: A Program for Missing Data10.18637/jss.v045.i07 ↗
21. Martinez, A., and Perez, A. (1999). Atlas Climatico de Galicia, Xunta de Galicia. Servicio Central.
22. Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series10.1002/hyp.9452 ↗
23. Quian, Z., Wang, L., Chen, X., Zhang, H., and Li, Z. (2022). Heteroscedastic Characteristics of Precipitation with Climate Changes in China. Atmosphere, 13.10.3390/atmos13122116 ↗