Leveraging remote sensing data with AHP and geospatial analysis for landslide susceptibility hotspot assessment in Bandarban of Bangladesh

Abstract

In the 21st century, climate change has exacerbated global instability, leading to a rise in landslide occurrences. In Bangladesh, mountainous areas such as Bandarban experience significant landslides during the monsoon season. This study seeks to evaluate landslide susceptibility in Bandarban and identify hotspots for optimal landslide hazard mitigation. This study examined landslide susceptibility using the analytical hierarchy process (AHP) and spatial weighted overlay (SWO). Ten conditioning factors were considered, with AHP based on re- sponses from 100 key respondents. Using field surveys and high-resolution satellite images, 280 landslide occurrence samples were collected to rank the subfactors. Using AHP-derived weights of factors and subfactors, the SWO approach was used to create the landslide susceptibility map (LSM). The Getis-Ord (Gi*) spatial sta- tistics was then used to generate landslide susceptibility hotspots. The result showed that human influence weight 17.02%, making it the most crucial factor in landslide susceptibility. AHP-derived weights were reliable because their consistency ratio was <0.1. According to the study, 59.86% of the area is moderately susceptible,20.06% is high, and 4.31% is very high. The validation of LSM by ROC curve found excellent performance (AUC = 0.93) of the approaches. Specifically, 63.8% of very high susceptibility areas and 33.26% of high susceptibility areas were found within the hotspot zones with 99% confidence. The research showed the combined use of field samples and remote sensing-based spatial variables improved the accuracy of LSM. These findings can be useful for ensuring proper land use planning and implementation of landslide hazard mitigation measures.

Keywords

Landslides / Susceptibility mapping / Hotspots analysis / Analytical hierarchy process (AHP) / Spatial weighted overlay (SWO) / Getis-Ord (Gi*) statistics

References

[1] B. Ganesh, S. Vincent, S. Pathan, S.R. Garcia Benitez, Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: growth and evolution, Remote Sens. Appl.: Society and Environment 29 ( 2023) 100905, https://doi.org/10.1016/j.rsase.2022.100905.

[2] P. Reichenbach, M. Rossi, B.D. Malamud, M. Mihir, F. Guzzetti, A review of statistically-based landslide susceptibility models, Earth Sci. Rev. 180 ( 2018) 60-91, https://doi.org/10.1016/j.earscirev.2018.03.001.

[3] B. Li, K. Liu, M. Wang, Q. He, Z. Jiang, W. Zhu, N. Qiao, Global dynamic rainfall- induced landslide susceptibility mapping using machine learning, Remote Sens. 14 ( 2022) 5795.

[4] B. Du, Z. Zhao, X. Hu, G. Wu, L. Han, L. Sun, Q. Gao, Landslide susceptibility prediction based on image semantic segmentation, Comput. Geosci. 155 ( 2021) 104860, https://doi.org/10.1016/j.cageo.2021.104860.

[5] B. Ahmed, The root causes of landslide vulnerability in Bangladesh, Landslides 18 ( 2021) 1707-1720, https://doi.org/10.1007/s10346-020-01606-0.

[6] S.I. Apu, N. Sharmili, MdY. Gazi, MdB. Mia, S.F. Sifa, Remote sensing and GIS- based landslide susceptibility mapping in a hilly district of Bangladesh: a comparison of different geospatial models, J. Indian Soc. Remote Sens. 53 ( 2025) 531-548, https://doi.org/10.1007/s12524-024-01988-x.

[7] M.D.T. Hossain, R.D. Chackroborty, L. Intisar, S. Al Toufiq Shuvo, A. Al Rakib, A.-Kafy, Landslide susceptibility and risk assessment in hilly regions of Bangladesh: a geostatistical and geospatial modeling approach for sustainability,in: G. K. Panda, R. Shaw, S.C. Pal, U. Chatterjee, A. Saha ( Landslide:Eds.), Susceptibility, Risk Assessment and Sustainability:Application of Geostatistical and Geospatial Modeling, Springer Nature Switzerland, Cham, 2024, pp. 593-619, https://doi.org/10.1007/978-3-031-56591-5_23.

[8] D. Wang, M. Hao, S. Chen, Z. Meng, D. Jiang, F. Ding, Assessment of landslide susceptibility and risk factors in China, Nat. Hazards 108 ( 2021) 3045-3059, https://doi.org/10.1007/s11069-021-04812-8.

[9] J.R. Araújo, A.M. Ramos, P.M.M. Soares, R. Melo, S.C. Oliveira, R.M. Trigo, Impact of extreme rainfall events on landslide activity in Portugal under climate change scenarios, Landslides 19 ( 2022) 2279-2293, https://doi.org/10.1007/s10346-022-01895-7.

[10] Q. Lin, Y. Wang, T. Glade, J. Zhang, Y. Zhang, Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China, Clim. Change 162 ( 2020) 761-779, https://doi.org/10.1007/s10584-020-02750-1.

[11] C. Scheidl, M. Heiser, S. Kamper, T. Thaler, K. Klebinder, F. Nagl, V. Lechner, G. Markart, W. Rammer, R. Seidl, The influence of climate change and canopy disturbances on landslide susceptibility in headwater catchments, Sci. Total Environ. 742 ( 2020) 140588, https://doi.org/10.1016/j.scitotenv.2020.140588.

[12] S. Panchal, A.K. Shrivastava, Landslide hazard assessment using analytic hierarchy process (AHP): a case study of National Highway 5 in India, Ain Shams Eng. J. 13 ( 2022) 101626, https://doi.org/10.1016/j.asej.2021.10.021.

[13] H.D. Skilodimou, G.D. Bathrellos, E. Koskeridou, K. Soukis, D. Rozos, Physical and anthropogenic factors related to landslide activity in the northern Peloponnese, Greece, Land 7 (3) ( 2018) 85.

[14] J. Abedin, Y.W. Rabby, I. Hasan, H. Akter, An investigation of the characteristics, causes, and consequences of June 13, 2017, landslides in Rangamati district Bangladesh, Geoenvironmental Disasters 7 ( 2020) 23, https://doi.org/10.1186/s40677-020-00161-z.

[15] Z. Ahmed, A.H.M.B. Hussain, S. Ambinakudige, M.N.Q. Ahmed, R. Alam, H.-A. Rezoan, D. Das Dola, M.M. Rahman, R. Hassan, S. Mahmud, Perceived human- induced causes of landslide in Chattogram Metropolitan area in Bangladesh, Earth Systems and Environment 6 ( 2022) 499-515, https://doi.org/10.1007/s41748-022-00304-2.

[16] M. Khatun, A.T.M.S. Hossain, H.M. Sayem, M. Moniruzzaman, Z. Ahmed, K.R. Rahaman, Landslide susceptibility mapping using weighted-overlay approach in Rangamati, Bangladesh, Earth Systems and Environment 7 ( 2023) 223-235, https://doi.org/10.1007/s41748-022-00312-2.

[17] B. Ahmed, Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong metropolitan area, Bangladesh, Landslides 12 ( 2015) 1077-1095, https://doi.org/10.1007/s10346-014-0521-x.

[18] Y.W. Rabby, Y. Li, Landslide susceptibility mapping using integrated methods: a case study in the Chittagong hilly areas, Bangladesh, Geosciences 10 ( 2020) 483.

[19] Y. Wu, W. Li, P. Liu, H. Bai, Q. Wang, J. He, Y. Liu, S. Sun, Application of analytic hierarchy process model for landslide susceptibility mapping in the gangu county,Gansu province, China, Environ. Earth Sci. 75 ( 2016) 422, https://doi.org/10.1007/s12665-015-5194-9.

[20] S. Moragues, M.G. Lenzano, M. Lanfri, S. Moreiras, E. Lannutti, L. Lenzano, Analytic hierarchy process applied to landslide susceptibility mapping of the north branch of argentino Lake, Argentina, Nat. Hazards 105 ( 2021) 915-941, https://doi.org/10.1007/s11069-020-04343-8.

[21] W. Chen, X. Xie, J. Wang, B. Pradhan, H. Hong, D.T. Bui, Z. Duan, J. Ma, A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, Catena 151 ( 2017) 147-160, https://doi.org/10.1016/j.catena.2016.11.032.

[22] Y. Sundriyal, S. Kumar, N. Chauhan, S. Kaushik, V. Kumar, N. Rana, R. Wasson, An integrated approach of machine learning and remote sensing for evaluating landslide hazards and risk hotspots, NW Himalaya, Remote Sens. Appl.: Society and Environment 33 ( 2024) 101140, https://doi.org/10.1016/j.rsase.2024.101140.

[23] T. Zhang, Y. Li, T. Wang, H. Wang, T. Chen, Z. Sun, D. Luo, C. Li, L. Han, Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping, Geosci. Lett. 9 ( 2022) 26, https://doi.org/10.1186/s40562-022-00236-9.

[24] Z. Li, J. Hu, K. Wu, J. Miao, J. Wu, Adjacent-atrous mechanism for expanding global receptive fields: an end-to-end network for multi-attribute scene analysis in remote sensing imagery, IEEE Trans. Geosci. Rem. Sens. 62 ( 2024) 1-19.

[25] Z. Li, J. Hu, K. Wu, J. Miao, J. Wu, Comprehensive attribute difference attention network for remote sensing image semantic understanding, IEEE Trans. Geosci. Rem. Sens. 63 ( 2024) 1-16. https://ieeexplore.ieee.org/abstract/document/10795154/. (Accessed 6 July 2025).

[26] A.-A. Kafy, M.M. Hasan, L. Ferdous, M.R. Ali, M.S. Uddin, Application of Artificial Hierarchy Process for Landslide Susceptibility Modelling in Rangamati Municipality Area, Bangladesh, 2019.

[27] M.S. Ullah, Geospatial modeling of landslide vulnerability and simulating spatial correlation with associated factors in Bandarban district, The Dhaka University Journal of Earth and Environmental Sciences 8 ( 2019) 51-66.

[28] BBS,District Statistics 2011, Bangladesh Bureau of Statistics (BBS), Dhaka, 2011. [29] M.S. Ullah, Mapping landslide and simulating spatial statistics: a case of Bandarban district of the Chittagong hill tracts, Orient. Geogr. 61 (1&2) ( 2017).

[29] M.S. Ullah, Mapping landslide and simulating spatial statistics: a case of Bandarban district of the Chittagong hill tracts, Orient. Geogr. 61 (1&2) (2017).

[30] P. Kayastha, Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from garuwa sub-basin, east Nepal, Arabian J. Geosci. 8 ( 2015) 8601-8613, https://doi.org/10.1007/s12517-015-1831-6.

[31] J. Dou, D. Tien Bui, A.P. Yunus, K. Jia, X. Song, I. Revhaug, H. Xia, Z. Zhu, Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan, PLoS One 10 ( 2015) e0133262, https://doi.org/10.1371/journal.pone.0133262.

[32] M. Gholami, E.N. Ghachkanlu, K. Khosravi, S. Pirasteh, Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method, J. Earth Syst. Sci. 128 ( 2019) 42, https://doi.org/10.1007/s12040-018-1047-8.

[33] H.R. Pourghasemi, B. Pradhan, C. Gokceoglu, Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at haraz watershed, Iran, Nat. Hazards 63 ( 2012) 965-996, https://doi.org/10.1007/s11069-012-0217-2.

[34] S.C. Pal, I. Chowdhuri, GIS-Based spatial prediction of landslide susceptibility using frequency ratio model of lachung river basin, North Sikkim, India, SN Appl. Sci. 1 ( 2019) 416, https://doi.org/10.1007/s42452-019-0422-7.

[35] H. Hong, S.A. Naghibi, H.R. Pourghasemi, B. Pradhan, GIS-based landslide spatial modeling in Ganzhou City, China, Arabian J. Geosci. 9 ( 2016) 112, https://doi. org/10.1007/s12517-015-2094-y.

[36] X. Yang, R. Liu, M. Yang, J. Chen, T. Liu, Y. Yang, W. Chen, Y. Wang, Incorporating landslide spatial information and correlated features among conditioning factors for landslide susceptibility mapping, Remote Sens. 13 ( 2021) 2166.

[37] C.-Y. Chen, W.-L. Huang, Land use change and landslide characteristics analysis for community-based disaster mitigation, Environ. Monit. Assess. 185 ( 2013) 4125-4139, https://doi.org/10.1007/s10661-012-2855-y.

[38] R. Pacheco Quevedo, A. Velastegui-Montoya, N. Montalván-Burbano, F. Morante-Carballo, O. Korup, C. Daleles Rennó Land use and land cover as a conditioning factor in landslide susceptibility: a literature review, Landslides 20 ( 2023)967-982.

[39] W. Fan, X. Wei, Y. Cao, B. Zheng, Landslide susceptibility assessment using the certainty factor and analytic hierarchy process, J. Mt. Sci. 14 ( 2017) 906-925, https://doi.org/10.1007/s11629-016-4068-2.

[40] A. El Jazouli, A. Barakat, R. Khellouk, GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco), Geoenvironmental Disasters 6 ( 2019) 1-12.

[41] H. Bourenane, M.S. Guettouche, Y. Bouhadad, M. Braham, Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods, Arabian J. Geosci. 9 ( 2016) 154, https://doi.org/10.1007/s12517-015-2222-8.

[42] T. Lukić D. Bjelajac, K.E. Fitzsimmons, S.B. Marković B. Basarin, D. Mlađan,T. Micić R.J. Schaetzl, M.B. Gavrilov, M. Milanović G. Sipos, G. Mezo}si,N. Knežević-Lukić M. Milinćić A. Létal I. Samardžić Factors triggering landslide occurrence on the Zemun Loess plateau, Belgrade area, Serbia, Environ. Earth Sci. 77 ( 2018) 519, https://doi.org/10.1007/s12665-018-7712-z.

[43] S. Jones, A.K. Kasthurba, A. Bhagyanathan, B.V. Binoy, Impact of anthropogenic activities on landslide occurrences in southwest India: an investigation using spatial models, J. Earth Syst. Sci. 130 ( 2021) 1-18.

[44] N. Intarawichian, S. Dasananda, Analytical hierarchy process for landslide susceptibility MAPPING IN lower MAE CHAEM WATERSHED, northern Thailand, Suranaree Journal of Science & Technology 17 ( 2010). https://www.thaiscience.info/journals/Article/SJST/10890515.pdf. (Accessed 8 October 2023).

[45] M.A. Syam, Heryanto, M.D. Balfas,Mapping of landslide susceptibility using analytical hierarchy process in Sukamaju Area, Tenggarong Seberang, Regency of Kutai Kartanegara, IOP Conf. Ser. Earth Environ. Sci. 279 ( 2019) 012002, https://doi.org/10.1088/1755-1315/279/1/012002.

[46] T.L. Saaty, Decision making with the analytic hierarchy process, Int. J. Serv. Sci.1 ( 2008) 83-98.[47] D. Barić H. Pilko, J. Strujić An analytic hierarchy process model to evaluate road section design, Transport 31 ( 2016) 312-321, https://doi.org/10.3846/16484142.2016.1157830.

[47] D. Barić, H. Pilko, J. Struji�c. An analytic hierarchy process model to evaluate road section design, Transport 31 (2016) 312–321, https://doi.org/10.3846/16484142.2016.1157830.

[48] D. Tešić J. Đorđević D. Ho€lbling, T. Đorđević D. Blagojević N. Tomić A. Lukić Landslide susceptibility mapping using AHP and GIS weighted overlay method: a case study from Ljig, Serbia, Serbian Journal of Geosciences 6 ( 2020) 9-21. [49] L. Ayalew, H. Yamagishi, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology 65 ( 2005) 15-31, https://doi.org/10.1016/j.geomorph.2004.06.010.

[49] L. Ayalew, H. Yamagishi, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology 65 (2005) 15–31, https://doi.org/10.1016/j.geomorph.2004.06.010.

[50] F.C. Dai, C.F. Lee, Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology 42 ( 2002) 213-228, https://doi. org/10.1016/S0169-555X(01)00087-3.

[51] J. Malczewski, GIS and Multicriteria Decision Analysis, Wiley, 1999.

[52] T.L. Saaty, The analytic hierarchy process: planning, priority setting. Resource Allocation, McGraw-Hill International Book Company, 1980.

[53] P.K. Shit, G.S. Bhunia, R. Maiti, Potential landslide susceptibility mapping using weighted overlay model (WOM), Modeling Earth Systems and Environment 2 ( 2016) 21, https://doi.org/10.1007/s40808-016-0078-x.

[54] A.M. Amiri, N. Nadimi, V. Khalifeh, M. Shams, GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques, Int. J. Inj. Control Saf. Promot. 28 ( 2021) 325-338.

[55] A. Qadir, S. Skakun, I. Becker-Reshef, N. Kussul, A. Shelestov, Estimation of sunflower planted areas in Ukraine during full-scale Russian invasion: insights from Sentinel-1 SAR data, Science of Remote Sensing 10 ( 2024) 100139, https://doi.org/10.1016/j.srs.2024.100139.

[56] A. Getis, J.K. Ord, The analysis of spatial Association by use of distance statistics, Geogr. Anal. 24 ( 1992) 189-206, https://doi.org/10.1111/j.1538-4632.1992.tb00261.x.

[57] F. Rossi, G. Becker, Creating forest management units with hot spot analysis (Getis- Ord Gi*) over a forest affected by mixed-severity fires, Aust. For. 82 ( 2019) 166-175.

[58] BBS,Population and Housing Census 2022, Bangladesh Bureau of Statistics (BBS), Dhaka, 2022.

[59] M.A. Islam, M.S. Islam, A.A. Jeet, A geotechnical investigation of 2017 Chattogram landslides, Geosciences 11 ( 2021) 337.

[60] M.S. Islam, M.J. Alam,Geological aspects of soil formation of Bangladesh, in:Proceedings of Geological Aspects of Soil Formation of Bangladesh Conference, 2009. Dhaka, Bangladesh.

[61] B. Ahmed, A. Dewan, Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh, Remote Sens. 9 ( 2017) 304.

[62] R. Barua, M. Muhibbullah, T.E. Alam, Hill cutting and landslide vulnerability: an environmental impact assessment approach on Bandarban town, Bangladesh, Chittagong University Journal of Biological Sciences ( 2019) 99-118.

[63] B. Nath, Quantitative assessment of forest cover change of a part of Bandarban hill tracts using NDVI techniques, Journal of Geosciences and Geomatics 2 (1) ( 2014) 21-27, https://doi.org/10.12691/jgg-2-1-4.

[64] I. Hossain, M.F. Hossain, Studies on the causes and impacts of landslide: a comparative study of Rangamati and Bandarban hilly district, Bangladesh, A Journal for Social Development 8 (1) ( 2018) 165-174.

[65] S.T. Islam, M.H. Miah, A. Jubaer, K.P. Mondal, M.M. Mahmud,An assessment of the causes and consequences of landslides ( 2017) occurred in the Southeastern areas of Bangladesh, Journal of Science and Technology Research 5 (1) ( 2023) 119-134.

[66] Y.W. Rabby, Y. Li, J. Abedin, S. Sabrina, Impact of land use/land cover change on landslide susceptibility in Rangamati Municipality of Rangamati District, Bangladesh, ISPRS Int. J. GeoInf. 11 (2) ( 2022) 89.

[67] M.S. Chowdhury, M.N. Rahman, M.S. Sheikh, M.A. Sayeid, K.H. Mahmud, B. Hafsa, GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh, Heliyon 10 ( 2024) e23424, https://doi.org/10.1016/j.heliyon.2023.e23424.

[68] N. Fuad, J. Meandad, A. Haque, R. Sultana, S. Anwar, S. Sultana, Landslide vulnerability analysis using frequency ratio (FR) model: a study on Bandarban district, Bangladesh 2407 ( 2024) 20239.

[69] J. Vojteková M. Vojtek, Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia, Geomat. Nat. Hazards Risk 11 ( 2020) 131-148, https://doi.org/10.1080/19475705.2020.1713233.

[70] M. Basharat, H.R. Shah, N. Hameed, Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan, Arabian J. Geosci. 9 ( 2016) 292, https://doi.org/10.1007/s12517-016-2308-y.

[71] N. Intarawichian, S. Dasananda, Analytical hierarchy process for landslide susceptibility mapping in lower Mae Cham Wateshed, Northern Thailand, Suranaree Journal of Science & Technology 17 (13) ( 2010).

[72] K. Tempa, K. Peljor, S. Wangdi, R. Ghalley, K. Jamtsho, S. Ghalley, P. Pradhan, UAV technique to localize landslide susceptibility and mitigation proposal: a case of Rinchending Goenpa landslide in Bhutan, Natural Hazards Research 1 ( 2021) 171-186.

[73] L.Q. Hung, N.T.H. Van, D.M. Duc, L.T.C. Ha, P. Van Son, N.H. Khanh, L.T. Binh, Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam), Landslides 13 ( 2016) 1285-1301.

[74] M.R. Mansouri Daneshvar, Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran, Landslides 11 ( 2014) 1079-1091, https://doi.org/10.1007/s10346-013-0458-5.

[75] B. Mandal, S. Mandal, Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India, Adv. Space Res. 62 ( 2018) 3114-3132, https://doi.org/10.1016/j.asr.2018.08.008.

[76] S. Mandal, R. Maiti, Application of analytical hierarchy process (AHP) and frequency ratio (FR) model in assessing landslide susceptibility and risk, in:S. Mandal, R. Maiti (Eds.), Semi-Quantitative Approaches for Landslide Assessment and Prediction, Springer Singapore, Singapore, 2015, pp. 191-226.

[77] Q. Wang, W. Li, A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping, Phys. Geogr. 38 ( 2017) 318-337, https://doi.org/10.1080/02723646.2017.1294522.

[78] J.M. Habumugisha, N. Chen, M. Rahman, M.M. Islam, H. Ahmad, A. Elbeltagi, G. Sharma, S.N. Liza, A. Dewan, Landslide susceptibility mapping with deep learning algorithms, Sustainability 14 ( 2022) 1734.

[79] A.L. Achu, C.D. Aju, M. Di Napoli, P. Prakash, G. Gopinath, E. Shaji, V. Chandra, Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis, Geosci. Front. 14 ( 2023) 101657.

[80] T. Kavzoglu, I. Colkesen, E.K. Sahin, Machine learning techniques in landslide susceptibility mapping: a survey and a case study,in: S. P. Pradhan, V. Vishal, T.N. Singh ( Landslides:Eds.), Theory, Practice and Modelling, Springer International Publishing, Cham, 2019, pp. 283-301.

[81] D. Myronidis, C. Papageorgiou, S. Theophanous, Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP), Nat. Hazards 81 ( 2016) 245-263, https://doi.org/10.1007/s11069-015-2075-1.

[82] M.S. Ullah, Geospatial modeling of potential landslide hazard estimation for better management in the Bandarban district of Bangladesh, Advances in Natural and Technological Hazards Research 52 ( 2024) 669-693, https://doi.org/10.1007/978-3-031-56591-5_26.

[83] BDRCS, Flash Flood and Landslide Response in Bandarban 2023, United States Agency for International Development, 2023.