Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process

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 infrastr...

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Main Authors: Rana Muhammad Amir Latif, Jinliao He
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Atmosphere
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Online Access:https://www.mdpi.com/2073-4433/16/1/22
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author Rana Muhammad Amir Latif
Jinliao He
author_facet Rana Muhammad Amir Latif
Jinliao He
author_sort Rana Muhammad Amir Latif
collection DOAJ
description 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.
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spelling doaj-art-248786bafd4b4605bbc9a683d8c8c6782025-01-24T13:21:43ZengMDPI AGAtmosphere2073-44332024-12-011612210.3390/atmos16010022Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy ProcessRana Muhammad Amir Latif0Jinliao He1The Center for Modern Chinese City Studies, School of Geographical Sciences, East China Normal University, Shanghai 200062, ChinaThe Center for Modern Chinese City Studies, Institute of Urban Development, East China Normal University, Shanghai 200062, ChinaFlood 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.https://www.mdpi.com/2073-4433/16/1/22flood susceptibilityremote sensing dataMCDAAHPPunjab Pakistan
spellingShingle Rana Muhammad Amir Latif
Jinliao He
Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
Atmosphere
flood susceptibility
remote sensing data
MCDA
AHP
Punjab Pakistan
title Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
title_full Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
title_fullStr Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
title_full_unstemmed Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
title_short Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
title_sort flood susceptibility mapping in punjab pakistan a hybrid approach integrating remote sensing and analytical hierarchy process
topic flood susceptibility
remote sensing data
MCDA
AHP
Punjab Pakistan
url https://www.mdpi.com/2073-4433/16/1/22
work_keys_str_mv AT ranamuhammadamirlatif floodsusceptibilitymappinginpunjabpakistanahybridapproachintegratingremotesensingandanalyticalhierarchyprocess
AT jinliaohe floodsusceptibilitymappinginpunjabpakistanahybridapproachintegratingremotesensingandanalyticalhierarchyprocess