Dergiler / International Journal of Environment and Geoinformatics / 2026 / Volume: 13 Issue: 1

Geospatial Approaches to Landslide Susceptibility Mapping: A Study of Rangamati Sadar Upazila, Bangladesh

Farhana Tazneen(Bangladesh Space Research and Remote Sensing Organization (SPARRSO))0009-0001-8292-2353
Sayfa
—
Yayın Tarihi
2026-04-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This demonstrates that conducting a thorough field survey is the best strategy to find landslides.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Landslide susceptibility mapping is one of the most effective tools for sustainable development and management in hilly regions of countries.
📊
Temel Bulgular & Veri:The landslide susceptibility map of the research area has been generated using the information value method and classified into multiple categories ranging from very low to very high susceptibility by integrating information values.
💡
Sonuç & Katkı:This demonstrates that conducting a thorough field survey is the best strategy to find landslides.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Susceptibility · Landslide Inventory · Landslide Factors

Abstract

Landslide susceptibility mapping is one of the most effective tools for sustainable development and management in hilly regions of countries. An innovative approach has been taken to establish landslide inventory, and a geospatial technique has been applied to map susceptible areas. The landslide susceptibility map of the research area has been generated using the information value method and classified into multiple categories ranging from very low to very high susceptibility by integrating information values. The analysis reveals high and very high susceptibility classes in the study area are influenced by higher slopes, relatively high topography, proximity to roads and streams, sandy and loamy soils, exposed Tipam sandstone, and Bokabil formation. 31.93% and 34.91% of the Rangamati Sadar Upazila area are high and very highly susceptible, while 7.24% are moderately susceptible. Balukhali Union is the most susceptible to landslide occurrence among the seven unions in Rangamati Sadar Upazila in terms of the high and very high susceptibility classes. It is impossible to differentiate between these landslides through image interpretation only because of the area's extensive vegetation, the poor quality of Google images, and the viewing angle of satellites. This demonstrates that conducting a thorough field survey is the best strategy to find landslides.

🔖

Bu Makaleye Atıf Yap

Tazneen, F. (2026). Geospatial Approaches to Landslide Susceptibility Mapping: A Study of Rangamati Sadar Upazila, Bangladesh. International Journal of Environment and Geoinformatics, Volume: 13 Issue: 1. https://ajindex.com/articles/1413062

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (53)

  1. Ahmed, B. (2013). Understanding the issues involved in urban landslide vulnerability in chittagong metropolitan area, bangladesh.
  2. Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077–1095. https://doi.org/10.1007/s10346-014-0521-x
  3. Ahmed, B. (2021). The root causes of landslide vulnerability in Bangladesh. Landslides, 18(5), 1707–1720. https://doi.org/10.1007/s10346-020-01606-0
  4. Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58, 21–44.
  5. Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1–2), 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
  6. Borga, M., Anagnostou, E. N., Blöschl, G., & Creutin, J. D. (2010). Flash floods: Observations and analysis of hydro-meteorological controls. In Journal of Hydrology (Vol. 394, Numbers 1–2, pp. 1–3). https://doi.org/10.1016/j.jhydrol.2010.07.048
  7. Brabb, E. E. (1985). Innovative approaches to landslide hazard and risk mapping. International Landslide Symposium Proceedings, Toronto, Canada, 1, 17–22.
  8. Cellek, S. (2021). The Effect of Aspect on Landslide and Its Relationship with Other Parameters. In Landslides. IntechOpen. https://doi.org/https://doi.org/10.5772/intechopen.99389
  9. Chen, Z., & Wang, J. (2007). Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Natural Hazards, 42, 75–89.
  10. El Jazouli, A., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), 1–12.
  11. Feizizadeh, B., & Blaschke, T. (2013). GIS-multicriteria decision analysis for landslide susceptibility mapping: Comparing three methods for the Urmia lake basin, Iran. Natural Hazards, 65(3), 2105–2128. https://doi.org/10.1007/s11069-012-0463-3
  12. Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology, 102(3–4), 85–98. https://doi.org/10.1016/J.ENGGEO.2008.03.022
  13. Gorsevski, P. V, Jankowski, P., & Gessler, P. E. (2006). An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control and Cybernetics, 35(1), 121–146.
  14. Gutiérrez, F., Linares, R., Roqué, C., Zarroca, M., Carbonel, D., Rosell, J., & Gutiérrez, M. (2015). Large landslides associated with a diapiric fold in Canelles Reservoir (Spanish Pyrenees): Detailed geological–geomorphological mapping, trenching and electrical resistivity imaging. Geomorphology, 241, 224–242.
  15. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1–4), 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1
  16. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Landslide hazard assessment in the Staffora basin, northern Italian Apennines. Geomorphology, 72, 272–299.
  17. Hervás, J. (2003). Lessons learnt from landslide disasters in Europe. European Communities Luxembourg.
  18. Hong, H., Miao, Y., Liu, J., & Zhu, A.-X. (2019). Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. Catena, 176, 45–64.
  19. Hossain, A. T. M. S., Toll, D. G., & Shushupti, O. (2020). Rainfall Induced Landslide Hazards of Bangladesh: Challenges, Issues and Sustainable Development. 11(7), 225–230.
  20. Hossain, S., Tushar, M. A. N., Islam, S., Razzak, M. Z. A., Hossain, S., & Rajib, M. (2023). Landslide Susceptibility Analysis of Geological Formations: A Case Study of the Dupi Tila Formation in the Rangamati Area. The Dhaka University Journal of Earth and Environmental Sciences, 11(2), 1–13. https://doi.org/10.3329/dujees.v11i2.68818
  21. Humayain, K. M., & Biplob, C. (2021). Landslide susceptibility mapping in the municipality of southeastern Bangladesh: The case of Rangamati district (Vol. 14, Number 6). https://pubs.usgs.gov/of/1997/ofr-97-
  22. Jamwal, A., Kanwar, N., & Kuniyal, J. C. (2020). Use of geographic information system for the vulnerability assessment of landscape in upper Satluj basin of district Kinnaur, Himachal Pradesh, India. Geology, Ecology, and Landscapes, 4(3), 171–186. https://doi.org/10.1080/24749508.2019.1608410
  23. Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186–190.
  24. Keefer, D. K. (2000). Statistical analysis of an earthquake-induced landslide distribution—the 1989 Loma Prieta, California event. Engineering Geology, 58(3–4), 231–249.
  25. Khan, Y. A., Lateh, H., Baten, M. A., & Kamil, A. A. (2012). Critical antecedent rainfall conditions for shallow landslides in Chittagong City of Bangladesh. Environmental Earth Sciences, 67(1), 97–106. https://doi.org/10.1007/s12665-011-1483-0
  26. Meena, S. R., Ghorbanzadeh, O., & Blaschke, T. (2019). A comparative study of statistics-based landslide susceptibility models: A case study of the region affected by the Gorkha earthquake in Nepal. ISPRS International Journal of Geo-Information, 8(2). https://doi.org/10.3390/ijgi8020094
  27. Mohammady, M., Pourghasemi, H. R., & Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221–236.
  28. Msilimba, G. G. A. Chizimba. (2007). A comparative study of landslides and geohazard mitigation in northern and central Malawi. University of the Free State.
  29. Pham, B. T., Prakash, I., Dou, J., Singh, S. K., Trinh, P. T., Tran, H. T., Le, T. M., Van Phong, T., Khoi, D. K., Shirzadi, A., & Bui, D. T. (2020). A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto International, 35(12), 1267–1292. https://doi.org/10.1080/10106049.2018.1559885
  30. Pourghasemi, H. R., & Kerle, N. (2016). Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental Earth Sciences, 75(3), 1–17. https://doi.org/10.1007/s12665-015-4950-1
  31. Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996. https://doi.org/10.1007/s11069-012-0217-2
  32. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geosciences, 51, 350–365. https://doi.org/10.1016/j.cageo.2012.08.023
  33. Rabby, Y. W., Hossain, M. B., & Abedin, J. (2022). Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods. Geocarto International, 37(12), 3371–3396. https://doi.org/10.1080/10106049.2020.1864026
  34. Rabby, Y. W., Hossain, M. B., & Hasan, M. (2019). Social vulnerability in the coastal region of Bangladesh: An investigation of social vulnerability index and scalar change effects. International Journal of Disaster Risk Reduction, 101329.
  35. Rabby, Y. W., & Li, Y. (2019). An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping. Landslides, 16(3), 633–645. https://doi.org/10.1007/s10346-018-1107-9
  36. Rabby, Y. W., & Li, Y. (2020a). Landslide inventory (2001–2017) of Chittagong hilly areas, Bangladesh. Data, 5(1). https://doi.org/10.3390/data5010004
  37. Rabby, Y. W., & Li, Y. (2020b). Landslide susceptibility mapping using integrated methods: A case study in the chittagong hilly areas, bangladesh. Geosciences (Switzerland), 10(12), 1–26. https://doi.org/10.3390/geosciences10120483
  38. Rai, P. K., Mohan, K., & Kumra, V. K. (2014). Landslide hazard and its mapping using remote sensing and GIS. Journal of Scientific Research, 58(1), 1–13.
  39. Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. In Earth-Science Reviews (Vol. 180, pp. 60–91). Elsevier B.V. https://doi.org/10.1016/j.earscirev.2018.03.001
  40. Sarkar1, S., Roy1, A. K., & Martha2, T. R. (2013). Landslide Susceptibility Assessment using Information Value Method in Parts of the Darjeeling Himalayas. In JOURNAL GEOLOGICAL SOCIETY OF INDIA (Vol. 82).
  41. Shano, L., Raghuvanshi, T. K., & Meten, M. (2020). Landslide susceptibility evaluation and hazard zonation techniques – a review. In Geoenvironmental Disasters (Vol. 7, Number 1, pp. 1–19). Springer. https://doi.org/10.1186/s40677-020-00152-0
  42. Shofiqul Islam, M., Shahidul Islam, M., Daiyan Ahmed, A. S., Ul Karim, S., Hosain, M., & Hoque, F. (2015). Seismic Induced Landslide Vulnerability in the Chittagong City, Bangladesh. stmjournals.
  43. Sultana, N. (2020). Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018. Cogent Social Sciences, 6(1), 2000–2018. https://doi.org/10.1080/23311886.2020.1737402
  44. Thapa, P. B. (2014). An approach towards integrated modelling of 3D geology and landslide susceptibility An approach towards integrated modelling of 3D geology and landslide susceptibility in the Lesser Himalaya of central Nepal. 65–76.
  45. Varnes, D. J. (1978). Slope movement types and processes. Special Report, 176, 11–33.
  46. Woldearegay, K. (2013). Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia: With implications for infrastructural development. Momona Ethiopian Journal of Science, 5(1), 3–31.
  47. Wubalem, A. (2021). Landslide Inventory, Susceptibility, Hazard and Risk Mapping. https://doi.org/DOI: http://dx.doi.org/10.5772/intechopen.100504
  48. Wubalem, A. (2022). The impact of DEM resolution on landslide susceptibility modeling. Arabian Journal of Geosciences, 15(10). https://doi.org/10.1007/s12517-022-10241-z
  49. Wubalem, A., & Meten, M. (2020a). Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Applied Sciences, 2, 1–19.
  50. Wubalem, A., & Meten, M. (2020b). Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Applied Sciences, 2(5). https://doi.org/10.1007/s42452-020-2563-0
  51. Yin, K. L. (1988). Statistical prediction model for slope instability of metamorphosed rocks. Proceedings of the 5th International Symposium on Landslides, 2, 1269–1272.
  52. Zhao, X., & Chen, W. (2020). Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142180
  53. Cellek, S. (2021). The Effect of Aspect on Landslide and Its Relationship with Other Parameters. In Landslides. IntechOpen. https://doi.org/https://doi.org/10.5772/intechopen.99389