Title: High-resolution precipitation prediction in Bangladesh via ensemble learning

Authors: Yichen Wu; Jiaxin Yang; Lipon Chandra Das; Zhihua Zhang; M. James C. Crabbe

Addresses: Climate Modeling Laboratory, School of Mathematics, Shandong University, Jinan, China ' Climate Modeling Laboratory, School of Mathematics, Shandong University, Jinan, China ' Climate Modeling Laboratory, School of Mathematics, Shandong University, Jinan, China; University of Chittagong, Chittagong, Bangladesh ' Climate Modeling Laboratory, School of Mathematics, Shandong University, Jinan, China ' Wolfson College, Oxford University, Oxford, UK; Institute of Biomedical and Environmental Science and Technology, University of Bedfordshire, Luton, UK

Abstract: As a developing agricultural country, Bangladesh is vulnerable to the effects of climate change, so accurate precipitation prediction is of great value to Bangladesh in achieving sustainable development. Traditional climate simulation models and prediction tools find it challenging to meet the growing needs on high spatial resolution. In this paper, we developed a XGBoost-based spatio-temporal precipitation prediction model and then generated high-resolution precipitation distribution maps in Bangladesh from 2025 to 2035, where the spatial resolution can reach 0.1° latitude and longitude. Finally, the EOF analysis reveals three leading modes in high-resolution precipitation evolution during 2025-2035.

Keywords: XGBoost model; precipitation prediction; Bangladesh.

DOI: 10.1504/IJGW.2024.139259

International Journal of Global Warming, 2024 Vol.33 No.3, pp.223 - 234

Received: 30 Jul 2023
Accepted: 20 Dec 2023
Published online: 28 Jun 2024 *

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