Flood Susceptibility Mapping Using Geographic Information System and Frequency Ratio Analysis in the Lang Suan Watershed, Southern Thailand

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Sirikorn Duangpiboon
Thongchai Suteerasak
Rawee Rattanakom
Wanchitra Towanlong

Abstract

Flood susceptibility map is one of the important tools for flood management planning. Therefore, the objective of this study was to produce a flood susceptibility map for the Lang Suan Watershed, Southern Thailand, by using Geographic Information System (GIS) and Frequency Ratio analysis (FR) a well-known statistical method for susceptibility mapping. In this study, 116 (70%) floods were used as training data and 8 flood conditioning factors, including 30-yr mean annual rainfall, road density, slope angle, elevation, drainage density, land use, soil drainage and distance from drainage for analysis, and 50 (30%) floods were used for validation of model by using the Area Under a Curve (AUC). The results showed that the very low flood susceptibility area was the most area (47.78%) whereas the very high flood susceptibility area was the least area (7.39%). On the other hand, the very high flood susceptibility area was the most community area (76 communities) whereas the low flood susceptibility area was the least community area (2 communities). Moreover, it found that the model was highly efficient. It produced a high accuracy value of AUC which had the success rate of 88.98% and the prediction rate of 84.98%. Hence, it confirmed that the result could be used for supporting flood management planning to administration and organization in the Lang Suan watershed, especially the local administration and organization in high and very high flood susceptibility area that was the dangerous area and population density (115 communities), such as Hat Yai, Wang Tako, Pho Daeng, Na Phaya and Ban Khaun Sub-District, Lang Suan District, Chumphon Province.

Article Details

Section
Engineering Research Articles

References

[1] D. Guhar-Sapir, P. Hoyois, and R. Below, Annual Disaster Statistical Review 2010: The Numbers and Trends, Brussels, Belgium: Centre for Research on the Epidemiology of Disasters (CRED), 2011, pp. 11–32.

[2] Thaipublica. (2012, Oct.). 10 Years Annual Disaster Statistical Review (1989–2009). Thailand [Online]. Available: http://thaipublica.org/wp-content/uploads/2012/10/สถิติภัยพิบัติของไทย.pdf

[3] Office of the National Economics and Social Development Board, Disaster Management and Recovery: A Case Study of Thailand and Other, Nonburi, Thailand: Petchrung, 2011, pp. 8–101.

[4] M. Lee, J. Kang, and S. Jeon, “Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS.” in IGRASS 2012 IEEE International Geoscience and Remote Sensing Conference, Munich, 2012, pp. 895–898.

[5] M. Sriwichai, “GIS for the preparation of disaster risk reduction, case study lakhok community, Muang Pathumthani,” Rangsit University Journal of Engineering and Technology, vol. 16, no. 2, pp. 1–9, 2013 (in Thai).

[6] S. Lee and T. Sambath, “Landslide susceptibility mapping in Damrei Romel area, Cambodia using frequency ratio and logistic regression models,” Environmental Geology, vol. 50, no. 6, pp. 847–855, 2006.

[7] S. Lee and B. Pradhan, “Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models,” Landslides, vol. 4, no. 1, pp. 33–41, 2007.

[8] M. Mohammady, H. R. Pourghasemi, and B. Pradhan, “Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weightsof-evidence models,” Journal of Asian Earth Sciences, vol. 61, pp. 221–236, 2012.

[9] A. Ozdemir and T. Altural, “A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey,” Journal of Asian Earth Sciences, vol. 64, pp. 180–197, 2013.

[10] A. D. Regmi, K. C. Devkota, K. Yoshida, B. Pradhan, H. R. Pourghasemi, T. Kumamoto, and A. Akgun, “Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya,” Arabian Journal of Geosciences, vol. 7, no. 2, pp. 725–742, 2014.

[11] N. Intarawichian, “A comparative study of analytical hierarchy process and probability analysis for landslide susceptibility zonation in lower Mae Chaem watershed, Northern Thailand,” Ph.D. dissertation, School of Geoinformatics, Suranaree University of Technology, 2008.

[12] N. Intarawichian and S. Dasananda, “Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand,” Environmental Earth Sciences, vol.64, no. 8, pp. 2271–2285, 2011.

[13] Bureau of Research, Development and Hydrology, The Division of Basin and Sub-Basin of Thailand, Bangkok, Thailand: Department of Water Resources, 2007, pp. 33.

[14] Department of Water Resources and Thammasat University, Research and Development Roles and Life of Community in Flood Area: A Case Study of Lang Suan Watershed, Southern Thailand, Bangkok, Thailand: Thammasat University Research and Consultancy Institiute (TU-RAC), 2013, pp. 148–174.

[15] Thai Meteorological Department. (2015). The Weather in Thailand. Climatological, Bangkok, Thailand [Online]. Available: https://www.tmd.go.th/info/climate_of_thailand-2524-2553.pdf

[16] O. Rahmati, H. R. Pourghasemi and H. Zeinivand, “Flood susceptibiltiy mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran,” Journal Geocarto International, vol. 31, no. 1, pp. 42–70, 2016.

[17] D. T. Bui, B. Pradhan, O. Lofman, I. Revhaug, and O. Dick, “Landslide susceptibility mapping at Hoa Binh Province (Vietnam) adaptive neurofuzzy inference system and GIS,” Computers & Geosciences, vol. 45, pp. 199–211, 2012.

[18] A. K. Jha, R. Bloch, and J. Lamond, Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century, Washington DC, U.S.A.: The World Bank, 2012, pp. 55–63.

[19] M. Sh. Tehrany, B. Pradhan, and M. N. Jebur, “Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS,” Journal of Hydrology, vol. 504, pp. 69–79, 2013.

[20] S. Duangpiboon, T. Suteerasak, and W. Towanlong, “Effects of geographical and topographical co-variables on rainfall interpolation in Lang Suan Watershed, Thailand.” in the 36th Asian Conference on Remote Sensing Conference, Quezon City, Metro Manila, Philippines, 2015, pp. 1–9.

[21] Land Development Department. (2016). Land use of Southern Thailand, Bangkok, Thailand [Online]. Available: http://www.ldd.go.th/web_OLP/report_research_S.html#south

[22] Office of Soil Resources Survey and Research, Characteristics and Properties of Established Soil Series in the Peninsular and Southeast Coast Regions of Thailand, Bangkok, Thailand: Land Development Department, 2005, pp. 1–98 (in Thai).

[23] A. M. Youssef, B. Pradhan, M. N. Jebur, and H. M. El-Harbi, “Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia,” Environmental Earth Sciences, vol. 73, no. 7, pp. 3745–3761, 2015.

[24] A. Jaafari, A. Najafi, J. Rezaeian, A. Sattarian, and I. Ghajar, “Planning road networks in landslideprone areas: A case study from the northern forests of Iran,” Land Use Policy, vol. 47, pp. 198–208, 2015.

[25] Y. Wu, W. Li, Q. Wang, Q. Liu, D. Yang, M. Xing, Y. Pei, and Sh. Yan, “Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for Gangu County, China,” Arabian Journal of Geosciences, vol. 9, no. 2, pp. 84–100, 2016.