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A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods

Chiranjit Singha, Neha Chakraborty, Satiprasad Sahoo, Quoc Bao Pham, Yunqing Xuan Orcid Logo

Natural Hazards, Volume: 121, Issue: 11, Pages: 13765 - 13810

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1007/s11069-025-07335-8

Abstract

This study evaluates the effectiveness of the analytic hierarchy process (AHP) based on six machine learning models in predicting flood susceptibility in the Dwarakeswar river basin in Eastern India. Fifteen flood conditioning factors were employed as input predictors. The dataset underwent a series...

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Published in: Natural Hazards
ISSN: 0921-030X 1573-0840
Published: Springer Nature 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69607
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spelling 2025-10-02T09:42:07.8770187 v2 69607 2025-06-02 A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2025-06-02 ACEM This study evaluates the effectiveness of the analytic hierarchy process (AHP) based on six machine learning models in predicting flood susceptibility in the Dwarakeswar river basin in Eastern India. Fifteen flood conditioning factors were employed as input predictors. The dataset underwent a series of pre-processing procedures, including conducting a statistical Pearson correlation, ordinary least squares (OLS), and multi-collinearity analysis, to identify the best flood-contributing factors. Additionally, the Information Gain Ratio (InGR) feature selection technique was utilized to assess the relevance of features. The accuracy of the models during the validation phases was assessed using various statistical metrics such as accuracy, kappa score, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC). Although all models demonstrated robust flood prediction abilities (AUC > 0.988), the AHP-Gradient Boosting Machine (GBM) model exhibited the highest performance (AUC = 0.996). This indicates that, among the models examined, the AHP-GBM model holds significant promise for evaluating flood-prone regions and facilitating effective planning and management of flood hazards. This model identified 12.68% and 5.14% of the study area as very high and high flood susceptibility zones, respectively. The SHapley Additive exPlanations (SHAP) analysis shows that the Modified Normalized Difference Water Index (MNDWI), rainfall, elevation, Normalized Difference Vegetation Index (NDVI), proximity to rivers, drainage density, and Terrain Ruggedness Indices (TRI) are the best influences on flood probability. Based on the climate projections from future Coupled Model Intercomparison Project Phase 6 (CMIP6) models (SSP2 4.5, SSP5 8.5), the southern region of the study area has been pinpointed as a hotspot for flooding vulnerability, with a susceptibility level classified as very high, encompassing 16.68% of the area. Journal Article Natural Hazards 121 11 13765 13810 Springer Nature 0921-030X 1573-0840 Flood susceptibility mapping (FSM); Analytic hierarchy process; Machine learning; Remote sensing 1 6 2025 2025-06-01 10.1007/s11069-025-07335-8 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee 2025-10-02T09:42:07.8770187 2025-06-02T10:51:57.5525973 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Chiranjit Singha 1 Neha Chakraborty 2 Satiprasad Sahoo 3 Quoc Bao Pham 4 Yunqing Xuan 0000-0003-2736-8625 5 69607__34368__86869f25ad8d4e188f87b4dd719ff6ad.pdf s11069-025-07335-8.pdf 2025-06-02T10:53:47.2853583 Output 7284575 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
spellingShingle A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
Yunqing Xuan
title_short A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
title_full A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
title_fullStr A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
title_full_unstemmed A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
title_sort A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods
author_id_str_mv 3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Yunqing Xuan
author2 Chiranjit Singha
Neha Chakraborty
Satiprasad Sahoo
Quoc Bao Pham
Yunqing Xuan
format Journal article
container_title Natural Hazards
container_volume 121
container_issue 11
container_start_page 13765
publishDate 2025
institution Swansea University
issn 0921-030X
1573-0840
doi_str_mv 10.1007/s11069-025-07335-8
publisher Springer Nature
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
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description This study evaluates the effectiveness of the analytic hierarchy process (AHP) based on six machine learning models in predicting flood susceptibility in the Dwarakeswar river basin in Eastern India. Fifteen flood conditioning factors were employed as input predictors. The dataset underwent a series of pre-processing procedures, including conducting a statistical Pearson correlation, ordinary least squares (OLS), and multi-collinearity analysis, to identify the best flood-contributing factors. Additionally, the Information Gain Ratio (InGR) feature selection technique was utilized to assess the relevance of features. The accuracy of the models during the validation phases was assessed using various statistical metrics such as accuracy, kappa score, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC). Although all models demonstrated robust flood prediction abilities (AUC > 0.988), the AHP-Gradient Boosting Machine (GBM) model exhibited the highest performance (AUC = 0.996). This indicates that, among the models examined, the AHP-GBM model holds significant promise for evaluating flood-prone regions and facilitating effective planning and management of flood hazards. This model identified 12.68% and 5.14% of the study area as very high and high flood susceptibility zones, respectively. The SHapley Additive exPlanations (SHAP) analysis shows that the Modified Normalized Difference Water Index (MNDWI), rainfall, elevation, Normalized Difference Vegetation Index (NDVI), proximity to rivers, drainage density, and Terrain Ruggedness Indices (TRI) are the best influences on flood probability. Based on the climate projections from future Coupled Model Intercomparison Project Phase 6 (CMIP6) models (SSP2 4.5, SSP5 8.5), the southern region of the study area has been pinpointed as a hotspot for flooding vulnerability, with a susceptibility level classified as very high, encompassing 16.68% of the area.
published_date 2025-06-01T08:18:58Z
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score 11.085163

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