High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework

Flood-related disasters have far-reaching impacts on infrastructure and societal well-being. Though characterizing flood susceptibilities using state-of-the-art approaches and modelling socio-economic exposure to highlight vulnerabilities is essential to assess and manage flood-associated risks, current studies are usually regional/coarser resolutions neglecting localized situations. Here we developed an integrated machine learning, artificial intelligence, and geospatial modelling-based framework for high-resolution flood susceptibility (30 m) and socio-economic exposure estimations at a larger scale using Pakistan as a case. To do so, the data on flooding, elevation, drainage, rainfall, Landsat-8 imagery, and gridded socio-economic layers were used. We produced the first national-scale high-resolution susceptibility maps for Pakistan, pinpointing areas at higher risk of flooding, and assessing the potential impact on the population and the economy. Our findings suggest that ∼29 % of the total area of Pakistan falls under critical flood susceptibility levels, with Sindh and Punjab being the most at-risk provinces. Notably, ∼95 million people (47 %) in Pakistan are exposed to high flood susceptibility with 74 % population of Sindh, 56 % of Punjab, and 33 % of Balochistan residing in high susceptibility areas. We further pinpoint economic hotspots in Sindh and upper Punjab as particularly vulnerable to flood risks, which calls for proactive disaster preparedness measures. Through the presented characterization of flood susceptibility and socio-economic exposure, our findings are useful to devise targeted interventions in highly exposed regions to enhance resilience and reduce the risks/impact of future floods. By addressing vulnerabilities and fostering resilience, Pakistan can effectively mitigate flood risks and safeguard its population and infrastructure. • The first national scale high-resolution (30m) flood susceptibility maps and exposure estimates are produced. • ∼29 % of the total area of Pakistan falls under critical flood susceptibility levels. • ∼95 million people (47 %) in Pakistan are exposed to high flood susceptibility. • Our population exposure estimates are ∼30 % larger than previously estimated. • Economic hotspots in Sindh and upper Punjab as particularly vulnerable to flood risks.

Authors

Institutions

Publication Details

Journal
International Journal of Disaster Risk Reduction
Published
2025-03-31
DOI
https://doi.org/10.1016/j.ijdrr.2025.105442
Citations
13
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
14.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework

Mirza Waleed, Muhammad Sajjad
13 citations
International Journal of Disaster Risk Reduction
Flood Risk Assessment and Management
14.37
article

High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework

Mirza Waleed, Muhammad Sajjad
article en
13 citations

Abstract

Flood-related disasters have far-reaching impacts on infrastructure and societal well-being. Though characterizing flood susceptibilities using state-of-the-art approaches and modelling socio-economic exposure to highlight vulnerabilities is essential to assess and manage flood-associated risks, current studies are usually regional/coarser resolutions neglecting localized situations. Here we developed an integrated machine learning, artificial intelligence, and geospatial modelling-based framework for high-resolution flood susceptibility (30 m) and socio-economic exposure estimations at a larger scale using Pakistan as a case. To do so, the data on flooding, elevation, drainage, rainfall, Landsat-8 imagery, and gridded socio-economic layers were used. We produced the first national-scale high-resolution susceptibility maps for Pakistan, pinpointing areas at higher risk of flooding, and assessing the potential impact on the population and the economy. Our findings suggest that ∼29 % of the total area of Pakistan falls under critical flood susceptibility levels, with Sindh and Punjab being the most at-risk provinces. Notably, ∼95 million people (47 %) in Pakistan are exposed to high flood susceptibility with 74 % population of Sindh, 56 % of Punjab, and 33 % of Balochistan residing in high susceptibility areas. We further pinpoint economic hotspots in Sindh and upper Punjab as particularly vulnerable to flood risks, which calls for proactive disaster preparedness measures. Through the presented characterization of flood susceptibility and socio-economic exposure, our findings are useful to devise targeted interventions in highly exposed regions to enhance resilience and reduce the risks/impact of future floods. By addressing vulnerabilities and fostering resilience, Pakistan can effectively mitigate flood risks and safeguard its population and infrastructure. • The first national scale high-resolution (30m) flood susceptibility maps and exposure estimates are produced. • ∼29 % of the total area of Pakistan falls under critical flood susceptibility levels. • ∼95 million people (47 %) in Pakistan are exposed to high flood susceptibility. • Our population exposure estimates are ∼30 % larger than previously estimated. • Economic hotspots in Sindh and upper Punjab as particularly vulnerable to flood risks.

International Journal of Disaster Risk ReductionVol. 121
Hong Kong Baptist University (HK), University of Hong Kong (HK)
Climate action
Openalex Percentile: Top 1%
Flood Risk Assessment and Management
14.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.