Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
Tallennettuna:
| Julkaisussa: | Atmosphere vol. 16, no. 1 (2025), p. 22 |
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| Päätekijä: | |
| Muut tekijät: | |
| Julkaistu: |
MDPI AG
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| Aiheet: | |
| Linkit: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3159426040 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos16010022 |2 doi | |
| 035 | |a 3159426040 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Rana Muhammad Amir Latif |u The Center for Modern Chinese City Studies, School of Geographical Sciences, East China Normal University, Shanghai 200062, China; <email>52263902018@stu.ecnu.edu.cn</email> | |
| 245 | 1 | |a Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. | |
| 651 | 4 | |a Punjab Pakistan | |
| 651 | 4 | |a Pakistan | |
| 653 | |a Hail | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Vertical polarization | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Susceptibility | ||
| 653 | |a Infrastructure | ||
| 653 | |a Remote sensing | ||
| 653 | |a Soil types | ||
| 653 | |a Sleet | ||
| 653 | |a Land use | ||
| 653 | |a Drainage density | ||
| 653 | |a Climate change | ||
| 653 | |a Wetness index | ||
| 653 | |a Decision analysis | ||
| 653 | |a Landslides & mudslides | ||
| 653 | |a Training | ||
| 653 | |a Decision making | ||
| 653 | |a Land cover | ||
| 653 | |a Mapping | ||
| 653 | |a Disaster relief | ||
| 653 | |a Hierarchies | ||
| 653 | |a Floods | ||
| 653 | |a Methods | ||
| 653 | |a Subjectivity | ||
| 653 | |a Geographical information systems | ||
| 653 | |a Rivers | ||
| 653 | |a Normalized difference vegetative index | ||
| 653 | |a Critical infrastructure | ||
| 653 | |a Flood predictions | ||
| 653 | |a Information systems | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Rainfall | ||
| 653 | |a Flood mapping | ||
| 653 | |a Risk assessment | ||
| 653 | |a Machine learning | ||
| 653 | |a Horizontal polarization | ||
| 653 | |a Precipitation | ||
| 653 | |a Support vector machines | ||
| 653 | |a Maps | ||
| 653 | |a Storm damage | ||
| 653 | |a Vegetation index | ||
| 700 | 1 | |a He, Jinliao |u The Center for Modern Chinese City Studies, Institute of Urban Development, East China Normal University, Shanghai 200062, China | |
| 773 | 0 | |t Atmosphere |g vol. 16, no. 1 (2025), p. 22 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159426040/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159426040/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159426040/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |