Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya

References

• Abdo HG. 2022. Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria. Int J Environ Sci Technol. 19(4):2599–2618. doi:10.1007/s13762-021-03322-1.
   Web of Science ®Google Scholar
• Abdo HG, Almohamad H, Al Dughairi AA, Ali SA, Parvin F, Elbeltagi A, Costache R, Mohammed S, Al-Mutiry M, Alsafadi K. 2022. Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria. Geosci Lett. 9(1):45. doi:10.1186/s40562-022-00256-5.
   Web of Science ®Google Scholar
• Anbalagan R, Singh B. 1996. Landslide hazard and risk assessment mapping of mountainous terrains—a case study from Kumaun Himalaya, India. Eng Geol. 43(4):237–246. doi:10.1016/S0013-7952(96)00033-6.
   Web of Science ®Google Scholar
• Atkinson PM, Massari R. 1998. Generalized linear modeling of susceptibility to land sliding in the central.
   Google Scholar
• Banavar JR, Maritan A, Volkov I. 2010. Applications of the principle of maximum entropy: from physics to ecology. J Phys Condens Matter. 22(6):063101. doi:10.1088/0953-8984/22/6/063101.
   PubMed Web of Science ®Google Scholar
• Barnard PL, Owen LA, Sharma MC, Finkel RC. 2001. Natural and human-induced landsliding in the Garhwal Himalaya of northern India. Geomorphology. 40(1–2):21–35. doi:10.1016/S0169-555X(01)00035-6.
   Web of Science ®Google Scholar
• Basharat M, Khan JA, Abdo HG, Almohamad H. 2023. An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan. Geomatics Nat Hazards Risk. 14(1):2210255. doi:10.1080/19475705.2023.2210255.
   PubMed Web of Science ®Google Scholar
• Benesty J, Chen J, Huang Y, Cohen I. 2009. Pearson correlation coefficient. In Noise reduction in speech processing. Berlin, Heidelberg: Springer; p. 1–4.
   Google Scholar
• Bhasin R, Grimstad E, Larsen JO, Dhawan AK, Singh R, Verma SK, Venkatachalam K. 2002. Landslide hazards and mitigation measures at Gangtok, Sikkim Himalaya. Eng Geol. 64(4):351–368. doi:10.1016/S0013-7952(01)00096-5.
   Web of Science ®Google Scholar
• Bordoni M,Galanti Y,Bartelletti C,Persichillo MG,Barsanti M,Giannecchini R,Avanzi GD,Cevasco A,Brandolini P,Galve JP, et al. 2020. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models. CATENA. 193:104630.doi:10.1016/j.catena.2020.104630.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random Forests. Mach Learn. 45(1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Brenning A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci. 5(6):853–862. doi:10.5194/nhess-5-853-2005.
   Web of Science ®Google Scholar
• Brown JL. 2014. SDM toolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol. 5(7):694–700. doi:10.1111/2041-210X.12200.
   Web of Science ®Google Scholar
• Brownlee J. 2021. Ensemble learning algorithms with Python: Make better predictions with bagging, boosting, and stacking. Machine Learning Mastery.
   Google Scholar
• Cantor SB,Sun CC,Tortolero-Luna G,Richards-Kortum R,Follen M. 1999. A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clin Epidemiol. 52(9):885–892. doi:10.1016/s0895-4356(99)00075-x. 10529029
   PubMed Web of Science ®Google Scholar
• Chen W,Pourghasemi HR,Kornejady A,Zhang N. 2017. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma. 305:314–327. doi:10.1016/j.geoderma.2017.06.020.
   Web of Science ®Google Scholar
• Chen W,Sun Z,Han J. 2019. Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Applied Sciences. 9(1):171.doi:10.3390/app9010171.
   Google Scholar
• Clark JD, Dunn JE, Smith KG. 1993. A multivariate model of female black bear habitat use for a geographic information
   Google Scholar
• Cortes C, Vapnik V. 1995. Support-vector networks. Mach Learn. 20(3):273–297. doi:10.1007/BF00994018.
   Web of Science ®Google Scholar
• Dahal RK, Hasegawa S. 2008. Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology. 100(3–4):429–443. doi:10.1016/j.geomorph.2008.01.014.
   Web of Science ®Google Scholar
• Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P. 2008. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology. 102(3–4):496–510. doi:10.1016/j.geomorph.2008.05.041.
   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(1):23–72. doi:10.1007/s11069-022-05554-x.
   Web of Science ®Google Scholar
• Dunning SA, Massey CI, Rosser NJ. 2009. Structural and geomorphological features of landslides in the Bhutan Himalaya derived from terrestrial laser scanning. Geomorphology. 103(1):17–29. doi:10.1016/j.geomorph.2008.04.013.
   Web of Science ®Google Scholar
• Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 17(1):43–57. doi:10.1111/j.1472-4642.2010.00725.x.
   Web of Science ®Google Scholar
• ERDAS Inc. 1999. Erdas field guide. Atlanta, GA: Erdas Inc.
   Google Scholar
• ESRI. 2008. ArcGIS 9.3. Redlands: Environmental Systems Research Institute.
   Google Scholar
• Farber O,Kadmon R. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Modell. 160(1-2):115–130. doi:10.1016/S0304-3800(02)00327-7. (Farber and Kadmon, 2003)
   Web of Science ®Google Scholar
• Felicísimo ÁM, Cuartero A, Remondo J, Quirós E. 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. 10(2):175–189. doi:10.1007/s10346-012-0320-1.
   Web of Science ®Google Scholar
• Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K. 2020. A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J Spat Sci. 65(3):401–418. doi:10.1080/14498596.2018.1505564.
   Google Scholar
• Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science. 310(5750):987–991. doi:10.1126/science.1116681.
   PubMed Web of Science ®Google Scholar
• Guo W, Ni X, Jing D, Li S. 2014. Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data. J Geogr Sci. 24(6):1009–1021. doi:10.1007/s11442-014-1134-y.
   Web of Science ®Google Scholar
• Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 72(1–4):272–299. doi:10.1016/j.geomorph.2005.06.002.
   Web of Science ®Google Scholar
• Haigh MJ, Rawat JS, Rawat MS, Bartarya SK, Rai SP. 1995. Interactions between forest and landslide activity along new highways in the Kumaun Himalaya. Forest Ecol Manag. 78(1–3):173–189. doi:10.1016/0378-1127(95)03584-5.
   Web of Science ®Google Scholar
• Hasegawa S, Dahal RK, Yamanaka M, Bhandary NP, Yatabe R, Inagaki H. 2009. Causes of large-scale landslides in the Lesser Himalaya of central Nepal. Environ Geol. 57(6):1423–1434. doi:10.1007/s00254-008-1420-z.
   Web of Science ®Google Scholar
• Hearn GJ, Hart AB. 2019. Landslide susceptibility mapping: a practitioner’s view. Bull Eng Geol Environ. 78:5811–5826.
   Web of Science ®Google Scholar
• Hejazi A, Javadi M, Musavi Kojabad N. 2022. Landslide Hazard Evaluation and Zoning with Fuzzy Logic (Case Study: Saraju River Basin).Geography (Regional Planning). (Hejazi, 2022)
   Google Scholar
• Hewitt K. 1998. Catastrophic landslides and their effects on the Upper Indus streams, Karakoram Himalaya, northern Pakistan. Geomorphology. 26(1–3):47–80. doi:10.1016/S0169-555X(98)00051-8.
   Web of Science ®Google Scholar
• HijmansRJPhillips S, Leathwick J, Elith J, Hijmans MRJ.2017.Package ‘dismo’. Circles, 9(1):1–68. (Hijmans, 2017)
   Google Scholar
• Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecol Model. 199(2):142–152. doi:10.1016/j.ecolmodel.2006.05.017.
   Web of Science ®Google Scholar
• Hong H,Liu J,Bui DT,Pradhan B,Acharya TD,Pham BT,Zhu A-X,Chen W,Ahmad BB. 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA. 163:399–413. doi:10.1016/j.catena.2018.01.005.
   Web of Science ®Google Scholar
• Huang Y,Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. CATENA. 165:520–529. doi:10.1016/j.catena.2018.03.003.
   Web of Science ®Google Scholar
• Javidan N,Kavian A,Pourghasemi HR,Conoscenti C,Jafarian Z,Rodrigo-Comino J. 2021. Evaluation of multi-hazard map produced using MaxEnt machine learning technique. Sci Rep. 11(1). doi: 10.1038/s41598-021-85862-7.
   PubMed Web of Science ®Google Scholar
• Kadavi P,Lee C-W,Lee S. 2018. Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sensing. 10(8):1252.doi:10.3390/rs10081252.
   Web of Science ®Google Scholar
• Karatzoglou A, Meyer D, Hornik K. 2006. Support vector machines in R. J Stat Soft. 15(9):1–28. doi:10.18637/jss.v015.i09.
   Web of Science ®Google Scholar
• Kaushik S, Rafiq M, Joshi PK, Singh T. 2020. Examining the glacial lake dynamics in a warming climate and GLOF modelling in parts of Chandra basin, Himachal Pradesh, India. Sci Total Environ. 714:136455. doi:10.1016/j.scitotenv.2019.136455.
   PubMed Web of Science ®Google Scholar
• Khattak GA, Owen LA, Kamp U, Harp EL. 2010. Evolution of earthquake-triggered landslides in the Kashmir Himalaya, northern Pakistan. Geomorphology. 115(1–2):102–108. doi:10.1016/j.geomorph.2009.09.035.
   Web of Science ®Google Scholar
• Kuncheva LI. 2004. Combining pattern classifiers: methods and algorithms, Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Kundu V, Patel RC. 2019. Susceptibility status of landslides in Yamuna valley, Uttarakhand, NW-Himalaya, India. Him Geol. 40(1):30–49.
   Google Scholar
• Lee S. 2019. Current and future status of GIS-based landslide susceptibility mapping: a literature review. Korean J Remote Sens. 35(1):179–193.
   Web of Science ®Google Scholar
• Mehrotra GS, Sarkar S, Kanungo DP, Mahadevaiah K. 1996. Terrain analysis and spatial assessment of landslide hazards in parts of Sikkim Himalaya. J Geol Soc India. 47:491–498.
   Web of Science ®Google Scholar
• Naceur HA, Abdo HG, Igmoullan B, Namous M, Almohamad H, Al Dughairi AA, Al-Mutiry M. 2022. Performance assessment of the landslide susceptibility modelling using the support vector machine, radial basis function network, and weight of evidence models in the N’fis river basin, Morocco. Geosci Lett. 9(1):39. doi:10.1186/s40562-022-00249-4.
   Web of Science ®Google Scholar
• Naimi B,Araújo MB. 2016. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography. 39(4):368–375. doi:10.1111/ecog.01881.
   Web of Science ®Google Scholar
• Owen LA, Kamp U, Khattak GA, Harp EL, Keefer DK, Bauer MA. 2008. Landslides triggered by the 8 October 2005 Kashmir earthquake. Geomorphology. 94(1–2):1–9. doi:10.1016/j.geomorph.2007.04.007.
   Web of Science ®Google Scholar
• Ozdemir A,Altural T. 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J. Asian Earth Sci. 64:180–197. doi:10.1016/j.jseaes.2012.12.014.
   Web of Science ®Google Scholar
• Park NW. 2015. Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environ Earth Sci. 73(3):937–949. doi:10.1007/s12665-014-3442-z.
   Web of Science ®Google Scholar
• Pham BT, Bui DT, Pourghasemi HR, Indra P, Dholakia MB. 2017. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol. 128(1–2):255–273. doi:10.1007/s00704-015-1702-9.
   Web of Science ®Google Scholar
• Phillips SJ, Dudík M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 31(2):161–175. doi:10.1111/j.0906-7590.2008.5203.x.
   Web of Science ®Google Scholar
• Pourghasemi HR,Kornejady A,Kerle N,Shabani F. 2020. Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. CATENA. 187:104364.doi:10.1016/j.catena.2019.104364.
   Web of Science ®Google Scholar
• 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. Comput Geosci. 51:350–365. doi:10.1016/j.cageo.2012.08.023.
   Web of Science ®Google Scholar
• Rabby YW,Li Y,Hilafu H. 2023. An objective absence data sampling method for landslide susceptibility mapping. Sci Rep. 13(1). doi:10.1038/s41598-023-28991-5.
   Web of Science ®Google Scholar
• Rahman G, Bacha AS, Ul Moazzam MF, Rahman AU, Mahmood S, Almohamad H, Al Dughairi AA, Al-Mutiry M, Alrasheedi M, Abdo HG. 2022. Assessment of landslide susceptibility, exposure, vulnerability, and risk in Shahpur Valley, eastern Hindu Kush. Front Earth Sci. 10:1348. doi:10.3389/feart.2022.953627.
   Web of Science ®Google Scholar
• Rokach L. 2010. Ensemble-based classifiers. Artif Intell Rev. 33:1–39. doi:10.1007/s10462-009-9124-7.
   Web of Science ®Google Scholar
• Roy J,Saha S,Arabameri A,Blaschke T,Bui DT. 2019. A Novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sensing. 11(23):2866.doi:10.3390/rs11232866.
   Web of Science ®Google Scholar
• Ruddell BL, Brunsell NA, Stoy P. 2013. Applying information theory in the geosciences to quantify process uncertainty, feedback, scale. Eos Trans AGU. 94(5):56–56. doi:10.1002/2013EO050007.
   Google Scholar
• Saha S,Saha A,Hembram TK,Mandal K,Sarkar R,Bhardwaj D. 2022. Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region. Stoch Environ Res Risk Assess. 36(10):3597–3616. doi:10.1007/s00477-022-02212-3.
   Web of Science ®Google Scholar
• Sangeeta, Singh SK. 2023. Influence of anthropogenic activities on landslide susceptibility: a case study in Solan district, Himachal Pradesh, India. J Mt Sci. 20(2):429–447. doi:10.1007/s11629-022-7593-1.
   Web of Science ®Google Scholar
• Sarkar S, Kanungo DP, Chauhan PKS. 2011. Varunavat landslide disaster in Uttarkashi, Garhwal Himalaya, India. QJEGH. 44(1):17–22. doi:10.1144/1470-9236/09-029.
   Web of Science ®Google Scholar
• Sarkar S, Kanungo DP, Mehrotra GS. 1995. Landslide hazard zonation: a case study in Garhwal Himalaya, India. Mt Res Dev. 15(4):301–309. doi:10.2307/3673806.
   Web of Science ®Google Scholar
• Shano L,Raghuvanshi TK,Meten M. 2020. Landslide susceptibility evaluation and hazard zonation techniques – a review. Geoenviron Disasters. 7(1). doi:10.1186/s40677-020-00152-0.
   Google Scholar
• Shu H,Guo Z,Qi S,Song D,Pourghasemi H,Ma J. 2021. Integrating landslide typology with weighted frequency ratio model for landslide susceptibility mapping: a case study from Lanzhou city of Northwestern China. Remote Sensing. 13(18):3623.doi:10.3390/rs13183623.
   Web of Science ®Google Scholar
• Singh AP, Dhadse K, Ahalawat J. 2019. Managing water quality of a river using an integrated geographically weighted regression technique with fuzzy decision making model. Environ Monit Assess. 191(6):378. doi:10.1007/s10661-019-7487-z.
   PubMed Web of Science ®Google Scholar
• Slingsby JA, Slingsby PW. 2019. Monitoring the critically endangered Clanwilliam cedar with freely available Google Earth imagery. PeerJ. 7:e7005. doi:10.7717/peerj.7005.
   PubMed Web of Science ®Google Scholar
• Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science. 240(4857):1285–1293. doi:10.1126/science.3287615.
   PubMed Web of Science ®Google Scholar
• Thuiller W,Lafourcade B,Engler R,Araújo MB. 2009. BIOMOD- a platform for ensemble forecasting of species distributions. Ecography. 32(3):369–373. doi:10.1111/j.1600-0587.2008.05742.x.
   Web of Science ®Google Scholar
• Thuillier G, Hersé M, Foujols T, Peetermans W, Gillotay D, Simon PC, Mandel H. 2003. The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions. Sol Phys. 214(1):1–22. doi:10.1023/A:1024048429145.
   Web of Science ®Google Scholar
• Tsangaratos P,Benardos A. 2014. Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards. 74(3):1489–1516. doi:10.1007/s11069-014-1245-x.
   Web of Science ®Google Scholar
• Wang Y,Sun D,Wen H,Zhang H,Zhang F. 2020. Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). IJERPH. 17(12):4206. doi:10.3390/ijerph17124206.
   Web of Science ®Google Scholar
• Weigel AP,Liniger MA,Appenzeller C. 2008. Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?. QJR Meteorol Soc. 134(630):241–260. doi:10.1002/qj.210.
   Web of Science ®Google Scholar
• Williams CJ, Lee SS, Fisher RA, Dickerman LH. 1999. A comparison of statistical methods for prenatal screening for Down syndrome. Appl Stochastic Models Bus Ind. 15(2):89–101. doi:10.1002/(SICI)1526-4025(199904/06)15:2<89::AID-ASMB366>3.0.CO;2-K.
   Web of Science ®Google Scholar
• Yao X, Dai FC. 2006. Support vector machine modeling of landslide susceptibility using a GIS: a case study. IAEG2006, 793.
   Google Scholar
• Yilmaz I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci. 61(4):821–836. doi:10.1007/s12665-009-0394-9.
   Web of Science ®Google Scholar
• Zhao Q,Chen W,Peng C,Wang D,Xue W,Bian H. 2022. Modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree. Environ Earth Sci. 81(15). doi:10.1007/s12665-022-10525-3.
   PubMed Web of Science ®Google Scholar
• Zimmermann NE, Edwards TC, Graham CH, Pearman PB, Svenning JC. 2010. New trends in species distribution modelling. Ecography. 33(6):985–989. doi:10.1111/j.1600-0587.2010.06953.x.
   Web of Science ®Google Scholar