Affiliation:
1. Geography and Geo-informatics Program, Faculty of Humanities and Social Sciences
2. Department of Geography, Faculty of Social Sciences, Srinakharinwirot University
3. Bureau of Quality Control of Livestock Products, Department of Livestock Development Ministry of Agriculture and Cooperatives
4. AAPICO ITS Company Limited, Hitech Industrial Estate
Abstract
Due to Tropical Storm Dianmu’s influence in the Lam Khan Chu watershed (LKCW) area, central Thailand saw its worst flood in 50 years from September 23 to September 28, 2021. The flooding lasted for 1-2 months. The objective of this research is to study flood susceptibility using logistic regression analysis in LCKW area. According to the study 11 floods occurred repeatedly between 2005 and 2021, in the southern of Bamnetnarong district and continued northeast to Chaturat district and Bueng Lahan swamp. These areas are the main waterways of the LKCW area, the Lam Khan Chu stream and the Huai Khlong Phai Ngam, for which the dominant flow patterns are braided streams. The main factors influencing flooding are geology, stream frequency, topographic wetness index, drainage density, soil, stream power index, land-use, elevation, mean annual precipitation, aspect, distance to road, distance to village, and distance to stream. The results of the logistic regression analysis shed light on these factors. All such variables were demonstrated by the β value coefficient. The area’s susceptibility to flooding was projected on a map, and it was discovered to have extremely high and high levels of susceptibility, encompassing regions up to 148.308 km2 (8.566%) and 247.421 km2 (14.291%), respectively, in the vicinity of the two main river sides of the watershed. As a result of this research the flood susceptibility map will be used as a guideline for future flood planning and monitoring.
Publisher
Russian Geographical Society
Subject
Environmental Science (miscellaneous),Geography, Planning and Development,Agricultural and Biological Sciences (miscellaneous)
Reference62 articles.
1. Al-Juaidi A.E.M., Nassar A.M. and Al-Juaidi, O.E.M. (2018). Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arabian Journal of Geosciences, 11, 765, DOI: 10.1007/s12517-018-4095-0.
2. AHA Centre, (2021). Tropical Cyclone Dianmu (21W), Lao PDR, Thailand and Viet Nam. [online] Available at: https://reliefweb.int/report/viet-nam/tropical-cyclone-dianmu-21w-lao-pdr-thailand-and-viet-nam-flash-update-2-28-sep-2021 [Accessed 28 Feb. 2022].
3. Bharath A., Kumar K.K., Maddamsetty R., Manjunatha M., Tangadagi R.B. and Preethi S. (2021). Drainage morphometry based subwatershed prioritization of Kalinadi basin using geospatial technology. Environmental Challenges, 5, 100277, DOI: 10.1016/j.envc.2021.100277.
4. Bui D.T., Ngo P.T.T., Pham T.D., Jaafari A., Minh N.Q., Hoa P.V. and Samui P. (2019). A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena, 179, 184-196, DOI: 10.1016/j.catena.2019.04.009.
5. Camara M., Jamil N.R.B., Abdullah A.F.B. and Hashim R.B. (2020). Integrating cellular automata Markov model to simulate future land-use change of a tropical basin. Global Journal of Environmental Science and Management, 6(3), 403-414, DOI: 10.22034/gjesm.2020.03.09.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献