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High-resolution precipitation prediction in Bangladesh via ensemble learning

  • Yichen Wu
    ,
  • Jiaxin Yang
    ,
  • Lipon Chandra Das
    ,
  • Zhihua Zhang
    ,
  • James Crabbe
  • Shandong University
    ,
  • University of Oxford
Research Output: Contribution to journal Article Peer-review

Open access

Sustainable Development Goals

  • SDG 13 - Climate Action
    SDG 13 Climate Action

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 223-234 (12 pages)

Journal (Volume, Issue Number)

International Journal of Global Warming (Volume 33, Issue 3)

Publication milestones

  • Accepted/In press - 20/12/2023
  • Published - 28/06/2024

Publication status

Published - 28/06/2024

ISSN

1758-2083

External Publication IDs

  • handle.net: 10547/626379
  • Scopus: 85197636917

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