Application of Geographic Information Systems for Flood Risk Assessment in Phran Kratai District, Kamphaeng Phet Province
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Abstract
This study aims to identify the factors influencing flood occurrences and to assess flood risk areas in Phran Kratai District, Kamphaeng Phet Province, by applying Geographic Information System (GIS) technology in combination with spatial potential analysis. Eight factors were considered: slope, elevation, rainfall, soil drainage, distance from water sources, historical flood areas, land use, and population density. The weight and score of each factor were determined based on secondary data obtained from 15 related research studies. The factors were categorized and weighted accordingly to establish criteria for flood risk assessment. The results revealed that slope and land use were the most influential factors in flood risk. Areas with slopes between 6–15 degrees and agricultural land were identified as highly flood-prone, accounting for 45.65% and 76.83% of the total risk areas, respectively. Interestingly, areas without previous flood records showed high to very high flood risk, representing 88.58% of the flood-prone zones. Flood risk levels were classified into five categories: moderate risk (32.68%), low risk (25.17%), high risk (23.89%), very high risk (10.43%), and very low risk (7.83%). Sub-districts with over 50% of their area falling into high or very high-risk categories included Khui Ban O, Huai Yang, and Wang Tabaek. The study recommends using the resulting flood risk maps as a tool for flood prevention and management planning in cooperation with relevant agencies. Key measures include designating flood retention zones, preserving natural buffer areas, regulating construction in high-risk areas, improving waterways and drainage systems, and enhancing preparedness in high-risk communities.
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References
Bui, D. T., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V. P., & Bin Ahmad, B. (2018). New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water, 10(9), 1210. https://doi.org/10.3390/w10091210
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651, 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064
Darabi, H., Choubin, B., Rahmati, O., Haghighi, A. T., Pradhan, B., & Kløve, B. (2019). Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of Hydrology, 569, 142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002
Feizizadeh, B., Blaschke, T., Nazmfar, H., & Rezaei Moghaddam, M. H. (2013). Landslide susceptibility mapping for the Urmia Lake Basin, Iran: A multi-criteria evaluation approach using GIS. International Journal of Environmental Research, 7(2), 319–336.
Gu, Z., Phakdimek, S., Nagami, K., & Komori, D. (2025). Relationship between urbanization–induced land use changes and flood risk: Case study in Chiang Mai, Thailand. Water, 17(3), 327.
Kamphaeng Phet Provincial Disaster Prevention and Mitigation Committee. (2015). Kamphaeng Phet Province disaster prevention and mitigation plan. https://www.disaster.go.th/upload/download/file_attach/5fad08305272b.pdf
Kamphaengphet Agricultural Extension Office. (2025). Agricultural information of Kamphaeng Phet Province. https://kamphaengphet.doae.go.th/province/?page_id=2271
Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2.
Kourgialas, N. N., & Karatzas, G. P. (2017). A national scale flood hazard mapping methodology: The case of Greece–Protection and adaptation policy approaches. Science of the Total Environment, 601, 441–452. https://doi.org/10.1016/j.scitotenv.2017.05.197
Land Development Department. (2020). Information on repeated flooding areas [Data set]. Land Development Department. https://data.go.th/dataset/floodplain
Land Development Department. (2024). Statistics on repeated flooding areas by sub-district [Data set]. https://data.go.th/dataset/ldd_21_04
Land Development Department (n.d.). Characteristics and important properties of soils. https://iddindee.ldd.go.th/web/data.html
Mangsamong, W., Mama, I., & Jedolo, N. (2022). Application of geographic information system for analysis of flood risk areas in Kolok River Basin, Narathiwat Province. In Proceedings of the 27th National Convention on Civil Engineering (NCCE27). Retrieved from https://conference.thaince.org/index.php/ncce27/article/view/1945/907
Niamthong, D., & Obphaet, A. (2023). Flood risk analysis in Bangkok Metropolitan Region. In Proceedings of the 28th National Convention on Civil Engineering, May 24–26, 2023, Phuket, Thailand.
Noichaisin, L. (2016). Application of GIS on flood risk area assessment in Sa Kaeo Province. Burapha Science Journal, 21(1), 51–63.
Sae-jern, N., Suwanchatree, N., Chub-uppakarn, T., & Chalermyanon, T. (2022). Assessment and comparison of urban flood vulnerability index: A case study of Hat Yai, Chiang Mai, and Ubon Ratchathani. Journal of Science and Technology, Maha Sarakham University, 41(3), 164–174.
Samanta, R. K., Bhunia, G. S., Shit, P. K., & Pourghasemi, H. R. (2018). Flood susceptibility mapping using geospatial frequency ratio technique: A case study of Subarnarekha River Basin, India. Modeling Earth Systems and Environment, 4, 395–408. https://doi.org/10.1007/s40808-018-0427-z
Sermkarndee, P., Charoensuk, J., & Inthasara, T. (2015). Analysis of flood risk areas using the geographic information system: Khuan Khanun District, Phatthalung Province. In Proceedings of the 11th Hatyai National and International Conference (pp. 2160–2173). Hatyai University.
Sureeyatanapas, P. (2016). Comparison of rank-based weighting methods for multi-criteria decision making. Engineering and Applied Science Research, 43, 376–379.
Tehrany, M. S., Jones, S., & Shabani, F. (2019). Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena, 175, 174–192. https://doi.org/10.1016/j.catena.2018.12.011
Thanarun, S., & Amornsanguansin, J. (2010). Application of geographic information systems in determining flood risk areas: Ang Thong Province. Environmental Management Journal, 6(2), 19–24.