Pre-processing satellite rainfall products improves hydrological simulations with machine learning-Reference-Cited by-同舟云学术

Pre-processing satellite rainfall products improves hydrological simulations with machine learning

Published:2024-07-26 Issue:10 Volume:69 Page:1356-1370
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Boulmaiz Tayeb¹^ORCID,Hafsi Radia²,Guermoui Mawloud³,Boutaghane Hamouda⁴,Abida Habib⁵,Saber Mohamed⁶,Kantoush Sameh A.⁶,Ferkous Khaled¹,Tramblay Yves⁷

Affiliation:

1. Materials, Energy Systems Technology and Environment Laboratory, Université de Ghardaia, Ghardaia, Algeria

2. Hydraulic and Civil Engineering Department, Université de Ghardaia, Ghardaia, Algeria

3. Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa, Algeria

4. Laboratory of Soil and Hydraulic, Badji Mokhtar Annaba University, Annaba, Algeria

5. Laboratory GEOMODELE, University of Sfax, Sfax, Tunisia

6. Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan

7. Espace-Dev (University of Montpellier, IRD), Montpellier, France

Funder

PRFU-MESRS Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2024.2378108

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4. Evaluation of Integrated Multisatellite Retrievals for GPM (IMERG) over Southern Canada against Ground Precipitation Observations: A Preliminary Assessment

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