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Evaluating Sinkhole Hazard Susceptibility Using Logistic Regression Model in Khlong I Pan Sub-Watershed, Surat Thani and Krabi Province, Thailand

https://doi.org/10.24057/2071-9388-2025-3431

Abstract

Sinkholes have frequently occurred over the past 20 years in the Khlong I Pan sub-watershed (KIPs) in Surat Thani and Krabi Province, Thailand. It was found that the earth collapsed more than 34 times. The objective of this research is to evaluate the sinkhole susceptibility using Logistic Regression (LR) analysis at the sub-watershed scale. This methodology used 14 variables affecting sinkhole occurrence to analyze the area, and create a sinkhole susceptibility map using LR. The results found that the variables that affect sinkhole formation include Well Density (WD), geology, Land Use (LU), Total Hardness (TH), Total Dissolved Solids (TDS), slope, Chlorine (Cl), distance to stream, elevation, Topographic Wetness Index (TWI), distance to village, soil, distance to active fault, and distance to well, respectively. All such variables are expressed by the exp β value coefficient. When prepared as a Karst sinkholes (KS) susceptibility map, it was found that a very high sinkhole susceptibility level covers an area of up to 399.86 km2 (19.16% of the total area). They appear mainly in the eastern region of the KIPs, especially at the confluence of the Khlong I Pan stream and the Khlong Trom stream. The other area is the central mountain range and the western mountain range, where geological structures with a casque topography are found. The results of this research suggest using the KS Susceptibility Map as a guideline for planning and monitoring potential future sinkholes.

About the Authors

Katawut Waiyasusri
Suan Sunandha Rajabhat University, Faculty of Humanities and Social Sciences, Geography and Geo-informatics Program
Thailand

1 U-Thong Nok Road, Dusit, Bangkok, 10300 



Parichat Wetchayont
Navamindradhiraj University, Faculty of Science and Health Technology, Disaster Management Program
Thailand

3 Khao Road, Wachira Phayaban, Dusit District, Bangkok, 10300 



Keerati Sripramai
Navamindradhiraj University, Faculty of Science and Health Technology, Disaster Management Program
Thailand

3 Khao Road, Wachira Phayaban, Dusit District, Bangkok, 10300 



References

1. Amin P., Ghalibaf M.A. Mermut, A.R. Delavarkhalafi, A. and Latifi,M.A. (2023). Prediction of sinkhole hazard using artificial intelligence model with soil characteristics and GPR data in arid alluvial land in Central Iran. Environmental Earth Sciences, 82(15), 372, DOI: 10.1007/s12665-023-11055-2.

2. Arora N. K. and Mishra I. (2023). Sustainable development goal 13: recent progress and challenges to climate action. Environmental Sustainability, 6(3), 297-301, DOI: 10.1007/s42398-023-00287-4.

3. Benammi M., Chaimanee Y., Jaeger J. J., Suteethorn V. and Ducrocq S. (2001). Eocene Krabi basin (southern Thailand): paleontology and magnetostratigraphy. Geological Society of America Bulletin, 113(2), 265-273, DOI: 10.1130/0016-7606(2001)113<0265:EKBSTP>2.0.CO;2.

4. Cahalan M. D. and Milewski A. M. (2018). Sinkhole formation mechanisms and geostatistical-based prediction analysis in a mantled karst terrain. Catena, 165, 333-344, DOI: 10.1016/j.catena.2018.02.010.

5. Cao Y., Jia H., Xiong J., Cheng W., Li K., Pang Q. and Yong Z. (2020). Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and LR Analyses in Fujian Province, China. ISPRS International Journal of Geo-Information, 9(12), 748, DOI: 10.3390/ijgi9120748.

6. Chen C. and Yu F. (2011). Morphometric analysis of debris flows and their source areas using GIS. Geomorphology, 129(3-4), 387–397, DOI: 10.1016/j.geomorph.2011.03.002.

7. Chen H., Oguchi T. and Wu P. (2018). Morphometric analysis of sinkholes using a semi-automatic approach in Zhijin County, China. Arabian Journal of Geosciences, 11, 412, DOI: 10.1007/s12517-018-3764-3.

8. Cvijić J. (1925). Karst i čovek (Karst and man). Glasnik Geografskog društva / Bulletin of the Geographical Society, 11, 1 –11.

9. de Castro Tayer, T. and Rodrigues P.C.H. (2021). Assessment of a semi-automatic spatial analysis method to identify and map sinkholes in the Carste Lagoa Santa environmental protection unit, Brazil. Environmental Earth Sciences, 80, 83, DOI: 10.1007/s12665-020-09354-z.

10. Dou J., Li X., Yunus A. P., Paudel U., Chang K. T., Zhu Z. and Pourghasemi H. R. (2015). Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach. Natural Hazards, 78, 1021-1044, DOI: 10.1007/s11069-015-1756-0.

11. Dou J., Li X., Yunus A. P., Paudel U., Chang K. T., Zhu Z. and Pourghasemi H. R. (2015). Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach. Natural Hazards, 78, 1021-1044, DOI: 10.1007/s11069-015-1756-0.

12. De Castro D.L., Bezerra F.H. and Oliveira Jr J.G. (2024). Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil. Open Geosciences, 16(1), 20220606, DOI: 10.1515/geo-2022-0606.

13. de Queiroz Salles L., Galvão P., Leal L.R.B., de Araujo Pereira R.G.F., da Purificação C.G.C. and Laureano F.V. (2018). Evaluation of susceptibility for terrain collapse and subsidence in karst areas, municipality of Iraquara, Chapada Diamantina (BA), Brazil. Environmental earth sciences, 77, 593, DOI: 10.1007/s12665-018-7769-8.

14. De Waele J. and Gutierrez F. (2022). Karst Hydrogeology, Geomorphology and Caves. Chichester: John Wiley & Son, DOI: 10.1002/9781119605379.

15. Dheeradilok P. (1995). Quaternary coastal morphology and deposition in Thailand. Quaternary International, 26, 49-54, DOI: 10.1016/1040-6182(94)00045-7.

16. Fakhruddin S. H. M. and Chivakidakarn, Y. (2014). A case study for early warning and disaster management in Thailand. International journal of disaster risk reduction, 9, 159-180, DOI: 10.1016/j.ijdrr.2014.04.008.

17. Frost-Killian S. (2008). Geohazards the risks beneath our feet. Quest, 4(2), 28-31.

18. Galvão P., Halihan T. and Hirata R. (2015). Evaluating karst geotechnical risk in the urbanized area of Sete Lagoas, Minas Gerais, Brazil. Hydrogeology Journal, 23(7), 1499, DOI: 10.1007/s10040-015-1266-x.

19. Galve J.P., Gutiérrez F., Remondo J., Bonachea J., Lucha P. and Cendrero A. (2009). Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain). Geomorphology, 111, 160–172, DOI: 10.1016/j.geomorph.2009.04.017.

20. Hamid H.T.A., Wenlong W. and Qiaomin L. (2020). Environmental sensitivity of flash flood hazard using geospatial techniques. Global Journal of Environmental Science and Management, 6(1), 31-46, DOI: 10.22034/GJESM.2020.01.03.

21. Hu J., Motagh M., Wang J., Qin F., Zhang J., Wu W. and Han Y. (2021). Karst collapse risk zonation and evaluation in Wuhan, China based on analytic hierarchy process, LR, and InSAR angular distortion approaches. Remote Sensing, 13(24), 5063, DOI: 10.3390/rs13245063.

22. Hu R.L., Yeung M.R., Lee C.F., Wang S.J. and Xiang J.X. (2001). Regional risk assessment of karst collapse in Tangshan, China. Environmental Geology, 40, 1377–1389, DOI: 10.1007/s002540100319.

23. Jia X.L., Dai Q.M. and Yang H.Z. (2019). Susceptibility zoning of karst geological hazards using machine learning and cloud model. Cluster Computing, 22(Suppl 4), 8051-8058, DOI: 10.1007/s10586-017-1590-0.

24. Kim K., Kim J., Kwak T.Y. and Chung C.K. (2018). LR model for sinkhole susceptibility due to damaged sewer pipes. Natural Hazards, 93, 765-785, DOI: 10.1007/s11069-018-3323-y.

25. Kim H.I., Han K.Y. and Lee J.Y. (2020). Prediction of Urban Flood Extent by LSTM Model and LR. KSCE Journal of Civil and Environmental Engineering Research, 40(3), 273–283, DOI: 10.12652/Ksce.2020.40.3.0273.

26. Kim Y. J., Nam B. H. and Youn H. (2019). Sinkhole detection and characterization using LiDAR-derived DEM with LR. Remote Sensing, 11(13), 1592, DOI: 10.3390/rs11131592.

27. Kelman I. (2015). Climate change and the Sendai framework for disaster risk reduction. International Journal of Disaster Risk Science, 6, 117-127, DOI: 10.1007/s13753-015-0046-5.

28. La Rosa A., Pagli C., Molli G., Francesco C., De Luca C., Amerino P. and D’Amato Avanzi G. A. (2018). Growth of a sinkhole in a seismic zone of the northern Apennines (Italy). Natural Hazards and Earth System Sciences, 18(9), 2355–2366, DOI: 10.5194/nhess-18-2355-2018.

29. Leknettip S., Chawchai S., Choowong M., Mueller D., Fülling A. and Preusser F. (2023). Sand ridges from the coastal zone of southern Thailand reflect late quaternary sea-level history and environmental conditions in Sundaland. Quaternary Science Reviews, 316, 108264, DOI: 10.1016/j.quascirev.2023.108264.

30. Maleki M., Salman M., Sahebi-Vayghan S. and Szabo S. (2023). GIS-based sinkhole susceptibility mapping using the best worst method. Spatial Information Research, 31(5), 537-545, DOI: 10.1007/s41324-023-00520-6.

31. Nam B., Kim Y. and Youn H. (2020). Identification and quantitative analysis of sinkhole contributing factors in Florida’s karst. Engineering Geology, 271, 105610, DOI: 10.1016/j.enggeo.2020.105610.

32. Nistor, M. and Nicula A. (2021). Application of GIS Technology for Tourism Flow Modelling in the United Kingdom. Geographia Technica, 16(1), 1-12, DOI: 10.21163/GT_2021.161.01.

33. Orhan O., Yakar M. and Ekercin S. (2020). An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems. Arabian Journal of Geosciences, 13, 886, DOI: 10.1007/s12517-020-05841-6.

34. Ozdemir A. (2016). Sinkhole susceptibility mapping using LR in Karapınar (Konya, Turkey). Bulletin of Engineering Geology and the Environment, 75, 681–707, DOI: 10.1007/s10064-015-0778-x.

35. Pondthai P., Arjwech R., Mathon K. and Taweelarp S. (2023). Investigation of Subsurface and Geological Structures Contributing to Collapse Sinkholes in Covered Karst Terrain, Northeast Thailand. Environment & Natural Resources Journal, 21(6), 513-523, DOI: 10.32526/ennrj/21/20230131.

36. Ramírez-Serrato N.L., García-Cruzado S.A., Herrera G.S., Yépez-Rincón F.D. and Villarreal S. (2024). Assessing the relationship between contributing factors and sinkhole occurrence in Mexico City, Geomatics, Natural Hazards and Risk, 15(1), 2296377, DOI: 10.1080/19475705.2023.2296377.

37. Siska P.P., Goovaerts P. and Hung I.-K. (2016). Evaluating susceptibility of karst dolines (sinkholes) for collapse in Sango, Tennessee, USA. Progress in Physical Geography: Earth and Environment, 40(4), 579-597, DOI: 10.1177/0309133316638816.

38. Sone M., Metcalfe I. and Chaodumrong P. (2012). The Chanthaburi terrane of southeastern Thailand: Stratigraphic confirmation as a disrupted segment of the Sukhothai Arc. Journal of Asian Earth Sciences, 61, 16-32, DOI: 10.1016/j.jseaes.2012.08.021.

39. Stefanov P., Prodanova H., Stefanova D., Stoycheva V. and Petkova G. (2023). Monitoring of water cycle in karst geosystems and its integration into ecosystem assessment framework. Journal of the Bulgarian Geographical Society, 48, 15-26, DOI: 10.3897/jbgs.e101301.

40. Subedi P., Subedi K., Thapa B. and Subedi P. (2019). Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and LR based approaches. Scientific Reports, 9, 7140, DOI: 10.1038/s41598-019-43705-6.

41. Szczuciński W. (2020). Postdepositional changes to tsunami deposits and their preservation potential. In: Engel, M., Pilarczyk, J., May, S.M., Brill, D., and Garrett E., ed., Geological records of tsunamis and other extreme waves. Amsterdam: Elsevier, 443–469, DOI: 10.1016/B978-0-12-815686-5.00021-3.

42. Trofimova E. (2018) Unesco World Karst Natural Heritage Sites: Geographical And Geological review. Geography, Environment, Sustainability, 11(2), 63-72, DOI: 10.24057/2071-9388-2018-11-2-63-72.

43. Udchachon M., Thassanapak H., Burrett C. and Feng Q. (2022). The boundary between the Inthanon Zone (Palaeotropics) and the

44. Gondwana-derived Sibumasu Terrane, northwest Thailand—evidence from Permo-Triassic limestones and cherts. Palaeobiodiversity and Palaeoenvironments, 102, 383–418, DOI: 10.1007/s12549-021-00508-w.

45. Veni G. (2002). Revising the karst map of the United States. Journal of Cave and Karst Studies, 64(1), 45–50.

46. Wei A., Li D., Zhou Y., Deng Q. and Yan L. (2021). A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model. Natural Hazards, 105, 405-430, DOI: 10.1007/s11069-020-04317-w.

47. Wood N.J., Doctor D.H., Alder J. and Jones J. (2023). Current and future sinkhole susceptibility in karst and pseudokarst areas of the conterminous United States. Frontiers in Earth Science, 11, 1207689, DOI: 10.3389/feart.2023.1207689.

48. Wu Y., Jiang X., Guan Z., Luo W. and Wang Y. (2018). AHP-based evaluation of the karst collapse susceptibility in Tailai Basin, Shandong Province, China. Environmental earth sciences, 77, 436, DOI: 10.1007/s12665-018-7609-x.

49. Xu X., Yan Y., Dai Q., Yi X., Hu Z. and Cen L. (2023). Spatial and temporal dynamics of rainfall erosivity in the karst region of southwest China: Interannual and seasonal changes. Catena, 221, 106763, DOI: 10.1016/j.catena.2022.106763.

50. Zeng Y. and Zhou W. (2019). Sinkhole remedial alternative analysis on karst lands. Carbonates and Evaporates, 34(1), 159-217, DOI: 10.1007/s13146-018-0467-5.

51. Zhou G., Yan H., Chen K. and Zhang R. (2016). Spatial analysis for susceptibility of second-time KS: A case study of Jili Village in Guangxi, China. Computers & geosciences, 89, 144-160, DOI: 10.1016/j.cageo.2016.02.001.


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Waiyasusri K., Wetchayont P., Sripramai K. Evaluating Sinkhole Hazard Susceptibility Using Logistic Regression Model in Khlong I Pan Sub-Watershed, Surat Thani and Krabi Province, Thailand. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(2):32-47. https://doi.org/10.24057/2071-9388-2025-3431

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)