Faramarzzadeh, M., Ehsani, M.R., Akbari, M. et al. Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa. Environ. Process. 10, 8 (2023). https://doi.org/10.1007/s40710-023-00625-y
Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa
|Author:||Faramarzzadeh, Marzie1; Ehsani, Mohammad Reza2; Akbari, Mahdi3;|
1Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland
2Department of Hydrology and Atmospheric Sciences, University of Arizona, 85721, Tucson, Arizona, AZ, USA
3Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland
4Department of Civil, Environmental and Geo-Engineering, University of Minnesota, 55455, Minneapolis, MN, USA
|Online Access:||PDF Full Text (PDF, 2.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230823103528
|Publish Date:|| 2023-08-23
Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).
|Pages:||1 - 16|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
This work is supported by the University of Oulu, Finland. Open Access funding provided by University of Oulu including Oulu University Hospital.
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.