Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa
Faramarzzadeh, Marzie; Ehsani, Mohammad Reza; Akbari, Mahdi; Rahimi, Reyhane; Moghaddam, Mohammad; Behrangi, Ali; Klöve, Björn; Torabi Haghighi, Ali; Oussalah, Mourad (2023-02-13)
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
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https://urn.fi/URN:NBN:fi-fe20230823103528
Tiivistelmä
Abstract
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).
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