Forecasting Climate Extremes-A Data Driven Approach
Volume 19, Issue 2, 2025
Download| Author(s): |
Rana Muhammad Ebrahim Usman Institute of Technology, Karachi, Pakistan, 21B-022-CS@alumni.uit.edu Muhammad Taha Pasha Usman Institute of Technology, Karachi, Pakistan, 21B-039-CS@alumni.uit.edu Hanzallah Ahmed Khan Usman Institute of Technology, Karachi, Pakistan, 21B-148-CS@students.uit.edu Muhammad Faraz Khan Usman Institute of Technology, Karachi, Pakistan, 21B-053-CS@alumni.uit.edu Muhammad Umer Usman Institute of Technology, Karachi, Pakistan, 21B-120-CS@students.uit.edu Muhammad Wasim Usman Institute of Technology, Karachi, Pakistan, mwasim@uit.edu Lubaid Ahmed* Usman Institute of Technology, Karachi, Pakistan, lahmed@uit.edu |
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| Abstract | Climate extremes such as heavy rainfall, droughts, and heatwaves pose an increased risk to human life, agriculture, buildings, and nature. Currently Pakistan relies on traditional statistical models that are built on limited station-level data, while machine-learning-based forecasts remain underutilized. The objective of this paper is to introduce a provincial-based approach for accurate and precise short-term extreme weather forecasts adapted to the unique climatic conditions of Pakistan. The research uses the Extreme Gradient Boosting algorithm along with 25 years of historical weather data of Pakistan retrieved from Numerical Weather Prediction models, specifically, GFS—Global Forecast System and ICON—Icosahedral Nonhydrostatic Model, in improving the prediction of heatwaves, droughts, and heavy rainfall events. The models showed excellent performance in identifying heatwaves, classifying droughts, and identifying rainfall severity. The results show the potential of refining data from physics-based weather models with machine learning models to significantly improve forecasts of climate extremes, filling a vital gap in Pakistan's weather prediction landscape. This approach would prove beneficial to emergency management agencies in disaster preparedness and response as well as to the general public to make better decisions. |
| Keywords | Climate extremes prediction, Machine Learning, XGBoost, Pakistan Climate, Heatwave prediction, Drought classification, Rainfall prediction. |
| Year | 2025 |
| Volume | 19 |
| Issue | 2 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/19.2/3.pdf | Page | 66-72 |