Flood risk assessment with machine learning: insights from the 2022 Pakistan mega-flood and climate adaptation strategies
Abstract Globally, the 2022 Pakistan mega-flood displaced over 33 million people and incurred economic losses exceeding $ 40 billion. By coupling seventy years of historical flood data with advanced machine learning techniques (GeoPINS within FloodCast), this study quantifies the event’s primary dri...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Natural Hazards |
| Online Access: | https://doi.org/10.1038/s44304-025-00096-1 |
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| Summary: | Abstract Globally, the 2022 Pakistan mega-flood displaced over 33 million people and incurred economic losses exceeding $ 40 billion. By coupling seventy years of historical flood data with advanced machine learning techniques (GeoPINS within FloodCast), this study quantifies the event’s primary drivers and projects future risk under climate change. Results show that the 2022 monsoon, amplified by low-pressure systems, delivered 7–8 times the 1990–2020 mean rainfall, flooding over 2100 streams and breaching 177 check dams. In Balochistan alone, these dam failures caused 80–85% of the province’s economic losses. Spatial–spectral analysis reveals that monsoon intensification, infrastructural vulnerability, and orographic forcing collectively govern inundation patterns. Under the SSP5 scenario, the area of high flood risk zones is projected to expand by 6.62% by 2080, even when modeling data-scarce regions. These findings underscore an urgent need for climate-resilient dam design, strategic sediment management, and adaptive flood-risk governance in similar vulnerable areas. |
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| ISSN: | 2948-2100 |