Assessment of landslide susceptibility of Pithoragarh, Uttarakhand (India) using logistic regression and multi-criteria decision-based analysis by analytical hierarchy process

References

• Ahmed KS, Basharat M, Riaz MT, Sarfraz Y, Shahzad A. 2021. Effect of soil microstructure on the small-strain shear modulus of saline soil. Arabian J Geosci. 14:1–19. doi:10.1007/s12517-020-06304-8.
   Google Scholar
• Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Ghorbani MA. 2021. GIS-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front. 12:857–876. doi:10.1016/j.gsf.2020.09.004.
   Web of Science ®Google Scholar
• Anbalagan R. 1992. Landslide hazard evaluation and zonation mapping in mountainous Terrain. Eng Geol. 32:269–277. doi:10.1016/0013-7952(92)90053-2.
   Web of Science ®Google Scholar
• Batar AK, Watanabe T. 2021. Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the Indian Himalayan region: recent developments, gaps, and future directions. ISPRS Int J Geoinf. 10:114. doi:10.3390/ijgi10030114.
   Web of Science ®Google Scholar
• Bera A, Mukhopadhyay BP, Das D. 2019. Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim. Nat Hazards. 96:935–959. doi:10.1007/s11069-019-03580-w.
   Web of Science ®Google Scholar
• Bilwa LM, Yanthan MA, Madesh P, Hutti B. 2015. Identification of potential landslide hazard zonation mapping using geoinformatics for Kohima region, Nagaland, India. Int J Innovat Sci Eng Techn. 2:1–10.
   Google Scholar
• Chauhan S, Sharma M, Arora MK. 2010. Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides. 7:411–423. doi:10.1007/s10346-010-0202-3.
   Web of Science ®Google Scholar
• Chawla A, Chawla S, Pasupuleti S, Rao ACS, Sarkar K, Dwivedi R. 2018. Landslide susceptibility mapping in Darjeeling Himalayas, India. Adv Civil Eng. 2018:1–17. doi:10.1155/2018/6416492.
   Web of Science ®Google Scholar
• Chen W, Chai H, Zhao Z, Wang Q, Hong H. 2016. Relationship between water-conservation behavior and water education in Guangzhou, China. Environ Earth Sci. 75:1–13. doi:10.1007/s12665-015-4873-x.
   Web of Science ®Google Scholar
• Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Roy P. 2020. Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci. 79:1–28. doi:10.1007/s12665-020-09227-5.
   Web of Science ®Google Scholar
• Dam ND, Amiri M, Al-Ansari N, Prakash I, Le HV, Nguyen HBT, Pham BT. 2022. Evaluation of Shannon entropy and weights of evidence models in landslide susceptibility mapping for the pithoragarh district of Uttarakhand state, India. Adv Civil Eng. doi:10.1155/2022/6645007.
   Web of Science ®Google Scholar
• Das G, Lepcha K. 2019. Grain texture as a proxy to understand porosity, permeability and density in Chandra Basin, India. SN Appl Sci. 1:1–22. doi:10.1007/s42452-018-0001-3.
   Web of Science ®Google Scholar
• Das I, Sahoo S, Van Westen C, Stein A, Hack R. 2010. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphol. 114:627–637. doi:10.1016/j.geomorph.2009.09.023.
   Web of Science ®Google Scholar
• Das S, Sarkar S, Kanungo DP. 2023. A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya. Nat Hazards. 115:23–72. doi:10.1007/s11069-022-05554-x.
   Web of Science ®Google Scholar
• Demir G. 2019. GIS-based landslide susceptibility mapping for a part of the north anatolian fault zone between reşadiye and koyulhisar (Turkey). Catena. 183:104211. doi:10.1016/j.catena.2019.104211.
   Web of Science ®Google Scholar
• Ercanoglu M, Gokceoglu C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol. 75:229–250. doi:10.1016/j.enggeo.2004.06.001.
   Web of Science ®Google Scholar
• Farooq S, Akram MS. 2021. Effect of soil microstructure on the small-strain shear modulus of saline soil. Arabian J Geosci. 14:1–16. doi:10.1007/s12517-020-06304-8.
   Google Scholar
• Fawcett T. 2006. An introduction to ROC analysis. Pattern Recognit Lett. 27:861–874. doi:10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Fayez L, Pazhman D, Pham BT, Dholakia MB, Solanki HA, Khalid M, Prakash I. 2018. Application of frequency ratio model for the development of landslide susceptibility mapping at part of Uttarakhand State, India. Int J Appl Eng Res. 13:6846–6854.
   Google Scholar
• Feizizadeh B, Blaschke T, Nazmfar H, Moghaddam R,HM. 2017. Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arabian J Geosci. 10:319–3336. doi:10.1007/s12517-017-3315-3.
   Web of Science ®Google Scholar
• Fernandez Merodo JA, Pastor M, Mira P. 2004. Modelling of diffuse failure mechanisms of catastrophic landslides. Comput Methods Appl Mech Eng. 193:2911–2939. doi:10.1016/j.cma.2003.09.016.
   Web of Science ®Google Scholar
• George D, Mallery P. 2000. SPSS for windows step-by step: a simple guide and reference, 2nd edition. Boston.: Allyn and Bacon.
   Google Scholar
• Ghosh S, Carranza EJM, Van Westen CJ, Jetten VG, Bhattacharya DN. 2011. Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology. 131:35–56. doi:10.1016/j.geomorph.2011.04.019.
   Web of Science ®Google Scholar
• Gupta RP, Kanungo DP, Arora MK, Sarkar S. 2008. Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf. 10:330–341. doi:10.1016/j.jag.2008.01.003.
   Web of Science ®Google Scholar
• Kannan M, Saranathan E, Anbalagan R. 2013. Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India. Arabian J Geosci. 6:2901–2913. doi:10.1007/s12517-012-0587-5.
   Web of Science ®Google Scholar
• Kanungo DP, Arora MK, Sarkar S, Gupta RP. 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol. 85:347–366. doi:10.1016/j.enggeo.2006.03.004.
   Web of Science ®Google Scholar
• Kavzoglu T, Sahin EK, Colkesen I. 2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. 11:425–439. doi:10.1007/s10346-013-0391-7.
   Web of Science ®Google Scholar
• Kumar A, Sharma RK, Bansal VK. 2018. The case of the Biscayne Bay and aquifer near Miami, Florida: density-driven flow of seawater or gravitationally driven discharge of deep saline groundwater? Env Earth Sci. 77:1–19. doi:10.1007/s12665-017-7169-5.
   Google Scholar
• Kumar A, Sharma RK, Bansal VK. 2019. A parametric study on the vertical pullout capacity of suction caisson foundation in cohesive soil. Innov Infrastruct Sol. 4:1–17. doi:10.1007/s41062-018-0188-6.
   Web of Science ®Google Scholar
• Kumar A, Sharma RK, Bansal VK. 2022. Spatial prediction of landslide hazard using GIS-multi-criteria decision analysis in kullu district of Himachal Pradesh, India. J Min Environ. 13(4):943–956. doi:10.22044/jme.2022.12235.2222.
   Web of Science ®Google Scholar
• Kumar A, Sharma RK, Mehta BS. 2020. Groundwater contamination in mega cities with finite sources. J Earth Syst Sci. 129:1–14. doi:10.1007/s12040-019-1281-8.
   Web of Science ®Google Scholar
• Kumar D, Thakur M, Dubey CS, Shukla DP. 2017. Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology. 295:115–125. doi:10.1016/j.geomorph.2017.06.013.
   Web of Science ®Google Scholar
• Kumar R, Anbalagan R. 2016. Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India. 87:271–286. doi:10.1007/s12594-016-0395-8.
   Web of Science ®Google Scholar
• Mandal B, Mandal S. 2018. Analytical hierarchy process (AHP) based landslide susceptibility mapping of lish river basin of eastern Darjeeling Himalaya, India. Adv Space Res. 62:3114–3132. doi:10.1016/j.asr.2018.08.008.
   Web of Science ®Google Scholar
• Mandal K, Saha S, Mandal S. 2021. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geosci Front. 12:101203. doi:10.1016/j.gsf.2021.101203.
   Web of Science ®Google Scholar
• Mathew J, Jha VK, Rawat GS. 2007. Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci. 628–638.
   Web of Science ®Google Scholar
• Mathew J, Jha VK, Rawat GS. 2009. Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides. 6:17–26. doi:10.1007/s10346-008-0138-z.
   Web of Science ®Google Scholar
• Meena SR, Tavakkoli Piralilou S. 2019. Comparison of earthquake-triggered landslide inventories: A case study of the 2015 Gorkha earthquake, Nepal. Geosciences (Basel). 9:437. doi:10.3390/geosciences9100437.
   Web of Science ®Google Scholar
• Nandi A, Shakoor A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol. 110:11–20. doi:10.1016/j.enggeo.2009.10.001.
   Web of Science ®Google Scholar
• Nautiyal A, Kumar A, Poddar A, Parajuli N. 2021. Optimum transportation of relief materials aftermath the disaster. J Achievem Mater Manufact Eng. 109(1):26–41. doi:10.5604/01.3001.0015.5857.
   Google Scholar
• Ngo TQ, Dam ND, Al-Ansari N, Amiri M, Phong TV, Prakash I, Pham BT. 2021. Landslide susceptibility mapping using single machine learning models: a case study from Pithoragarh District, India. Adv Civil Eng. 1–19. doi:10.1155/2021/9934732.
   Web of Science ®Google Scholar
• Ohlmacher GC. 2007. Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol. 91:117–134. doi:10.1016/j.enggeo.2007.01.005.
   Web of Science ®Google Scholar
• Panchal S, Shrivastava AK. 2022. Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India. Ain Shams Eng J. 13:101626. doi:10.1016/j.asej.2021.10.021.
   Web of Science ®Google Scholar
• Pandey VK, Pourghasemi HR, Sharma MC. 2020. Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto Int. 35:168–187. doi:10.1080/10106049.2018.1510038.
   Web of Science ®Google Scholar
• Pareta K, Kumar J, Pareta U. 2012. Solutions of fractional zakharov-kuznetsov equations By FCT method; this paper has been withdrawn. Int J Eng Techn. 1:1–9. doi:10.14419/ijet.v1i1.14.
   Google Scholar
• Peethambaran B, Anbalagan R, Kanungo DP, Goswami A, Shihabudheen KV. 2020. A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas. Catena. 195:104751. doi:10.1016/j.catena.2020.104751.
   Web of Science ®Google Scholar
• Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Bui DT. 2019. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: hybrid machine learning approaches. Catena. 175:203–218. doi:10.1016/j.catena.2018.12.018.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Prakash I, Dholakia M. 2015. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach frequency ratio method. Int J Eng Res Techn. 4:338–344.
   Google Scholar
• Pradhan B, Lee S. 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw. 25:747–759. doi:10.1016/j.envsoft.2009.10.016.
   Web of Science ®Google Scholar
• Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC. 2013. Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards. 65:315–330. doi:10.1007/s11069-012-0365-4.
   Web of Science ®Google Scholar
• Saaty T. 1980. The analytic hierarchy process (AHP) for decision making. In Kobe, Japan. 1–69.
   Google Scholar
• Saaty TL. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol. 15:234–281. doi:10.1016/0022-2496(77)90033-5.
   Web of Science ®Google Scholar
• Saaty TL. 2000. The fundamentals of decision making and priority theory with the analytic hierarchy process. Pennsylvia: University of Pittsburgh (Vol 1).
   Google Scholar
• Saha A, Saha S. 2020. Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: a study on Kurseong region of Darjeeling Himalaya, India. Remot Sens Appl Soc Environ. 19:100323. doi:10.1016/j.rsase.2020.100323.
   Google Scholar
• Sarkar S, Kanungo DP. 2004. An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Remot Sens. 70:617–625. doi:10.14358/PERS.70.5.617.
   Web of Science ®Google Scholar
• Sharma LP, Patel N, Debnath P, Ghose MK. 2012. Assessing landslide vulnerability from soil characteristics – a GIS-based analysis. Arabian J Geosci. 5:789–796. doi:10.1007/s12517-010-0272-5.
   Web of Science ®Google Scholar
• Sharma RK, Kaur A, Kumar A. 2019. Slope stability analysis by bishop analysis using MATLAB program based on particle swarm optimization technique. In: Proceedings of the 1st international conference on sustainable waste management through design: IC_SWMD 2018 1. Springer International Publishing; p. 285–293. doi:10.1007/978-3-030-02707-0_34.
   Google Scholar
• Shrestha S, Kang TS, Choi JC. 2018. The monsoon system: land–sea breeze or the ITCZ? J Earth Syst Sci. 127:1–17. doi:10.1007/s12040-017-0916-x.
   Web of Science ®Google Scholar
• Singh K, Kumar V. 2017. Late cretaceous (cenomanian-turonian) ammonites from southern Morocco and south western Algeria. Arabian J Geosci. 10:1–18. doi:10.1007/s12517-016-2714-1.
   Web of Science ®Google Scholar
• Tran TH, Dam ND, Jalal FE, Al-Ansari N, Ho LS, Phong TV, Pham BT. 2021. GIS-based soft computing models for landslide susceptibility mapping: A case study of pithoragarh district, Uttarakhand state, India. Math Probl Eng. doi:10.1155/2021/9914650.
   Web of Science ®Google Scholar
• Varnes JD. 1984. Landslide Hazard Zonation: a review of principles and practice. Natural Hazards.
   Google Scholar
• Veerappan R, Negi A, Siddan A. 2017. Landslide susceptibility mapping and comparison using frequency ratio and analytical hierarchy process in part of NH-58, Uttarakhand, India. In: Advancing culture of living with landslides: volume 2 advances in landslide science. Springer International Publishing; p. 1081–1091.
   Google Scholar
• Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena. 72:1–12. doi:10.1016/j.catena.2007.01.003.
   Web of Science ®Google Scholar
• Yilmaz I. 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci. 35:1125–1138. doi:10.1016/j.cageo.2008.08.007.
   Web of Science ®Google Scholar