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Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm

Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm

Ismail Elhassnaoui, Zineb Moumen, Hicham Ezzine, Marwane Bel-lahcen, Ahmed Bouziane, Driss Ouazar, Moulay Driss Hasnaoui
Copyright: © 2022 |Pages: 29
ISBN13: 9781668436868|ISBN10: 1668436868|EISBN13: 9781668436875
DOI: 10.4018/978-1-6684-3686-8.ch026
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MLA

Elhassnaoui, Ismail, et al. "Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm." Research Anthology on Environmental and Societal Impacts of Climate Change, edited by Information Resources Management Association, IGI Global, 2022, pp. 510-538. https://doi.org/10.4018/978-1-6684-3686-8.ch026

APA

Elhassnaoui, I., Moumen, Z., Ezzine, H., Bel-lahcen, M., Bouziane, A., Ouazar, D., & Hasnaoui, M. D. (2022). Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm. In I. Management Association (Ed.), Research Anthology on Environmental and Societal Impacts of Climate Change (pp. 510-538). IGI Global. https://doi.org/10.4018/978-1-6684-3686-8.ch026

Chicago

Elhassnaoui, Ismail, et al. "Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm." In Research Anthology on Environmental and Societal Impacts of Climate Change, edited by Information Resources Management Association, 510-538. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3686-8.ch026

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Abstract

In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.

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