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Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

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Listed:
  • Pornnapa Panyadee

    (OASYS Research Group, Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Paskorn Champrasert

    (OASYS Research Group, Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Floods cause disastrous damage to the environment, economy, and humanity. Flood losses can be reduced if adequate management is implemented in the pre-disaster period. Flood hazard maps comprise disaster risk information displayed on geo-location maps and the potential flood events that occur in an area. This paper proposes a spatiotemporal flood hazard map framework to generate a flood hazard map using spatiotemporal data. The framework has three processes: (1) temporal prediction, which uses the LSTM technique to predict water levels and rainfall for the next time; (2) spatial interpolation, which uses the IDW technique to estimate values; and (3) map generation, which uses the CNN technique to predict flood events and generate flood hazard maps. The study area is Chiang Mai Province, Thailand. The generated hazard map covers 20,107 km 2 . There are 14 water-level telemetry stations and 16 rain gauge stations. The proposed model accurately predicts water level and rainfall, as demonstrated by the evaluation results (RMSE, MAE, and R 2 ). The generated map has a 95.25 % mean accuracy and a 97.25 % mean F1-score when compared to the actual flood event. The framework enhances the accuracy and responsiveness of flood hazard maps to reduce potential losses before floods occur.

Suggested Citation

  • Pornnapa Panyadee & Paskorn Champrasert, 2024. "Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand," Sustainability, MDPI, vol. 16(11), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4433-:d:1400555
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    References listed on IDEAS

    as
    1. Madhurima Ganguly & Rahul Aynyas & Abhishek Nandan & Prasenjit Mondal, 2018. "Hazardous area map: an approach of sustainable urban planning and industrial development—a review," 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. 91(3), pages 1385-1405, April.
    2. Tahir Ali Akbar & Azka Javed & Siddique Ullah & Waheed Ullah & Arshid Pervez & Raza Ali Akbar & Muhammad Faisal Javed & Abdullah Mohamed & Abdeliazim Mustafa Mohamed, 2022. "Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    3. Chotirose Prathom & Paskorn Champrasert, 2023. "General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    4. Fan Yang & Guangqiu Huang & Yanan Li, 2023. "A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
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