Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms
Abstract Accurate flood susceptibility mapping (FSM) is a control approach to flood management. This research introduces a novel approach to increase the accuracy of FSM by optimizing the CatBoost algorithm with two swarm-based metaheuristic algorithms: the Zebra optimization algorithm (ZOA) and the...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12022-6 |
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| author | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Sani I. Abba Jamil Hussain Soo-Mi Choi |
| author_facet | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Sani I. Abba Jamil Hussain Soo-Mi Choi |
| author_sort | Seyed Vahid Razavi-Termeh |
| collection | DOAJ |
| description | Abstract Accurate flood susceptibility mapping (FSM) is a control approach to flood management. This research introduces a novel approach to increase the accuracy of FSM by optimizing the CatBoost algorithm with two swarm-based metaheuristic algorithms: the Zebra optimization algorithm (ZOA) and the Whale optimization algorithm (WOA). This research addresses the critical gap in determining optimal hyperparameters for machine learning models in FSM. Existing studies often ignore the importance of hyperparameter tuning in FSM, leading to suboptimal model performance. This research seeks to enhance and improve the accuracy of FSM in Shushtar County, located in southwest Iran, by utilizing the CatBoost-WOA and CatBoost-ZOA algorithms. In this research, FSM was conducted by using 13 parameters that affect floods and flood occurrence points as inputs. The evaluation results of the flood susceptibility maps showed an accuracy of 84.2% for CatBoost, 85% for CatBoost-WOA, and 87.2% for CatBoost-ZOA. This represents a 3.0% absolute improvement in accuracy with the ZOA-optimized model over the non-optimized CatBoost. The results of this study demonstrated that integrating swarm-based methods with machine learning boosting algorithms significantly enhanced FSM accuracy. The results of this study, as a non-structural approach, can help managers and decision-makers in flood management and control. |
| format | Article |
| id | doaj-art-0638c3ff800c44729071604ece03eade |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0638c3ff800c44729071604ece03eade2025-08-20T03:45:53ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-12022-6Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithmsSeyed Vahid Razavi-Termeh0Abolghasem Sadeghi-Niaraki1Sani I. Abba2Jamil Hussain3Soo-Mi Choi4Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong UniversityDepartment of Computer Science and Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong UniversityDepartment of Civil Engineering, Prince Mohammad Bin Fahd UniversityDepartment of Artificial Intelligence Data Science, Sejong UniversityDepartment of Computer Science and Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong UniversityAbstract Accurate flood susceptibility mapping (FSM) is a control approach to flood management. This research introduces a novel approach to increase the accuracy of FSM by optimizing the CatBoost algorithm with two swarm-based metaheuristic algorithms: the Zebra optimization algorithm (ZOA) and the Whale optimization algorithm (WOA). This research addresses the critical gap in determining optimal hyperparameters for machine learning models in FSM. Existing studies often ignore the importance of hyperparameter tuning in FSM, leading to suboptimal model performance. This research seeks to enhance and improve the accuracy of FSM in Shushtar County, located in southwest Iran, by utilizing the CatBoost-WOA and CatBoost-ZOA algorithms. In this research, FSM was conducted by using 13 parameters that affect floods and flood occurrence points as inputs. The evaluation results of the flood susceptibility maps showed an accuracy of 84.2% for CatBoost, 85% for CatBoost-WOA, and 87.2% for CatBoost-ZOA. This represents a 3.0% absolute improvement in accuracy with the ZOA-optimized model over the non-optimized CatBoost. The results of this study demonstrated that integrating swarm-based methods with machine learning boosting algorithms significantly enhanced FSM accuracy. The results of this study, as a non-structural approach, can help managers and decision-makers in flood management and control.https://doi.org/10.1038/s41598-025-12022-6Flood-prone area mappingFlood managementBoosting algorithmSwarm-based metaheuristicsSemi-arid climate |
| spellingShingle | Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Sani I. Abba Jamil Hussain Soo-Mi Choi Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms Scientific Reports Flood-prone area mapping Flood management Boosting algorithm Swarm-based metaheuristics Semi-arid climate |
| title | Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| title_full | Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| title_fullStr | Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| title_full_unstemmed | Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| title_short | Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| title_sort | flood prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms |
| topic | Flood-prone area mapping Flood management Boosting algorithm Swarm-based metaheuristics Semi-arid climate |
| url | https://doi.org/10.1038/s41598-025-12022-6 |
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