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Flood susceptibility mapping in river basins: a risk analysis using AHP-TOPISIS-2 N support and vector machine

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  • Admir José Giacon

    (São Paulo State University)

  • Alexandre Marco Silva

    (São Paulo State University)

Abstract

Due to the damage caused by floods, mapping areas susceptible to this natural phenomenon plays a fundamental role in environmental planning. Therefore, it becomes essential to understand and map the conditions and factors involved in areas affected by geo-hydro-meteorological events. In this context, we mapped areas susceptible to flooding using the AHP-TOPSIS-2 N, Support Vector Machine (SVM), and a hybrid model, AHP-SVM, the Sorocaba-Medio Tiete basin, that is a subtropical, densely populated river basin located in Brazilian territory. We considered 11 conditioning factors related to hydrogeomorphological and anthropological characteristics, and 382 historical flood and non-flood points. We assessed the accuracy of the modeling using the Area Under the Curve – AUC. The AHP-SVM model presented the best efficiency among the models analyzed (AUC = 0.962). The principal conditioning factors related to flooding were land cover and land use. We argue that models can be successfully applied as a scientific tool in the mapping of areas susceptible to flooding by public managers and risk managers since the resultant maps can help mitigate the negative impacts related to the flood event.

Suggested Citation

  • Admir José Giacon & Alexandre Marco Silva, 2025. "Flood susceptibility mapping in river basins: a risk analysis using AHP-TOPISIS-2 N support and vector machine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(3), pages 3239-3266, February.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:3:d:10.1007_s11069-024-06924-3
    DOI: 10.1007/s11069-024-06924-3
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    References listed on IDEAS

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    1. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.
    2. Olabi, A.G. & Obaideen, Khaled & Elsaid, Khaled & Wilberforce, Tabbi & Sayed, Enas Taha & Maghrabie, Hussein M. & Abdelkareem, Mohammad Ali, 2022. "Assessment of the pre-combustion carbon capture contribution into sustainable development goals SDGs using novel indicators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Camille Parmesan & Gary Yohe, 2003. "A globally coherent fingerprint of climate change impacts across natural systems," Nature, Nature, vol. 421(6918), pages 37-42, January.
    4. Chinh Luu & Quynh Duy Bui & Romulus Costache & Luan Thanh Nguyen & Thu Thuy Nguyen & Tran Phong & Hiep Le & Binh Thai Pham, 2021. "Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 3229-3251, September.
    5. Wanderson Luiz-Silva & Antonio Carlos Oscar-Júnior, 2022. "Climate extremes related with rainfall in the State of Rio de Janeiro, Brazil: a review of climatological characteristics and recorded trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 713-732, October.
    6. Eduardo Alves Neder & Fabiano Araújo Moreira & Michele Dalla Fontana & Roger Rodrigues Torres & David Montenegro Lapola & Maria da Penha Costa Vasconcellos & Ana Maria Barbieri Bedran-Martins & Arlind, 2021. "Urban adaptation index: assessing cities readiness to deal with climate change," Climatic Change, Springer, vol. 166(1), pages 1-20, May.
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