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Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB

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  • Mahya Norallahi

    (Jundi-Shapur University of Technology)

  • Hesam Seyed Kaboli

    (Jundi-Shapur University of Technology)

Abstract

Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data.

Suggested Citation

  • Mahya Norallahi & Hesam Seyed Kaboli, 2021. "Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB," 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. 106(1), pages 119-137, March.
  • Handle: RePEc:spr:nathaz:v:106:y:2021:i:1:d:10.1007_s11069-020-04453-3
    DOI: 10.1007/s11069-020-04453-3
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    1. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," 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. 116(3), pages 3797-3816, April.
    2. Shanzhong Qi & Shufen Cao & Shunli Hu & Qian Liu, 2024. "Bibliometric analysis on urban flood and waterlogging disasters during the period of 1998—2022," 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. 120(14), pages 12595-12612, November.
    3. Zhongping Zeng & Yujia Li & Jinyu Lan & Abdur Rahim Hamidi, 2021. "Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    4. Maelaynayn El baida & Mohamed Hosni & Farid Boushaba & Mimoun Chourak, 2024. "A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 5823-5864, December.
    5. Chao Ma & Wenchao Qi & Hongshi Xu & Kai Zhao, 2022. "An integrated quantitative framework to assess the impacts of disaster-inducing factors on causing urban flood," 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. 113(3), pages 1903-1924, September.
    6. Fatemeh Rezaie & Mahdi Panahi & Sayed M. Bateni & Changhyun Jun & Christopher M. U. Neale & Saro Lee, 2022. "Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping," 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(2), pages 1247-1283, November.
    7. Bahram Choubin & Farzaneh Sajedi Hosseini & Omid Rahmati & Mansor Mehdizadeh Youshanloei, 2023. "A step toward considering the return period in flood spatial modeling," 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. 115(1), pages 431-460, January.
    8. Maelaynayn El baida & Farid Boushaba & Mimoun Chourak & Mohamed Hosni & Hichame Sabar, 2024. "Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco," 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. 120(11), pages 10013-10041, September.

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