{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T13:32:46Z","timestamp":1772803966805,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T00:00:00Z","timestamp":1541635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Fund of China","award":["41330636"],"award-info":[{"award-number":["41330636"]}]},{"name":"Graduate Innovation Fund of Jilin University","award":["2017137"],"award-info":[{"award-number":["2017137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4\u20135 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos\u2019 interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development.<\/jats:p>","DOI":"10.3390\/ijgi7110438","type":"journal-article","created":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T03:08:02Z","timestamp":1541732882000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":110,"title":["Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China"],"prefix":"10.3390","volume":"7","author":[{"given":"Xiaohui","family":"Sun","sequence":"first","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Jianping","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Yiding","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Xudong","family":"Han","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Jiewei","family":"Zhan","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6635-8035","authenticated-orcid":false,"given":"Wei","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s100640050066","article-title":"Landslide hazard assessment: Summary review and new perspectives","volume":"58","author":"Aleotti","year":"1999","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10346-004-0039-8","article-title":"An approach for GIS-based statistical landslide susceptibility zonation\u2014With a case study in the himalayas","volume":"2","author":"Saha","year":"2005","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.geomorph.2006.04.007","article-title":"Estimating the quality of landslide susceptibility models","volume":"81","author":"Guzzetti","year":"2006","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s11069-017-3145-3","article-title":"Correction to: Landslide susceptibility mapping of the sera river basin using logistic regression model","volume":"91","author":"Raja","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_6","unstructured":"Brabb, E.E., Pampeyan, E.H., and Bonilla, M.G. (1972). Landslide Susceptibility in San Mateo County, California. [Ph.D. Thesis, Stanford University]."},{"key":"ref_7","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"Gis-based logistic regression for landslide susceptibility mapping of the zhongxian segment in the three gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.cageo.2008.08.007","article-title":"Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey)","volume":"35","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/10106049.2016.1140824","article-title":"A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping","volume":"32","author":"Chen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., and Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability, 8.","DOI":"10.3390\/su8090948"},{"key":"ref_13","first-page":"1","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji city, China","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.geomorph.2012.04.024","article-title":"Application of kernel-based fisher discriminant analysis to map landslide susceptibility in the Qinggan river delta, Three Gorges, China","volume":"171\u2013172","author":"He","year":"2012","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00704-016-1919-2","article-title":"Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: A comparison between GLM, GAM, MARS, and M-AHP methods","volume":"130","author":"Pourghasemi","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1002\/esp.3998","article-title":"Exploiting maximum entropy method and aster data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy)","volume":"41","author":"Lombardo","year":"2016","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_17","first-page":"1","article-title":"Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naive bayes, multilayer perceptron neural networks, and functional trees methods","volume":"122","author":"Pham","year":"2015","journal-title":"Theor. Appl. Climatol."},{"key":"ref_18","first-page":"314","article-title":"Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0013-7952(03)00142-X","article-title":"Determination and application of the weights for landslide susceptibility mapping using an artificial neural network","volume":"71","author":"Lee","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1130\/0016-7606(2000)112<413:LCTHDI>2.0.CO;2","article-title":"Late Cenozoic to Holocene deformation in southwestern Sichuan and Adjacent. Yunnan, China, and its role in formation of the southeastern part of the Tibetan Plateau","volume":"112","author":"Wang","year":"2000","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.geomorph.2005.05.011","article-title":"Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses","volume":"72","author":"Can","year":"2005","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_23","first-page":"487","article-title":"Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides","volume":"12","author":"Yang","year":"2010","journal-title":"Int. J Appl. Earth Obs."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cao, C., Wang, Q., Chen, J., Ruan, Y., Zheng, L., Song, S., and Niu, C. (2016). Landslide susceptibility mapping in vertical distribution law of precipitation area: Case of the Xulong hydropower station reservoir, Southwestern China. Water, 8.","DOI":"10.3390\/w8070270"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, F., Xu, P., Wang, C., Wang, N., and Jiang, N. (2017). Application of a GIS-based slope unit method for landslide susceptibility mapping along the Longzi river, southeastern Tibetan plateau, China. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6060172"},{"key":"ref_26","first-page":"1206","article-title":"Analysis of landslide influential factors and coupling intensity based on third theory of quantification","volume":"29","author":"Li","year":"2010","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_27","first-page":"468","article-title":"Landslide risk assessment based on combination weighting-unascertained measure theory","volume":"34","author":"Li","year":"2013","journal-title":"Rock Soil Mech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","article-title":"Landslide susceptibility assessment using SVM machine learning algorithm","volume":"123","author":"Bajat","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.enggeo.2006.03.004","article-title":"A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas","volume":"85","author":"Kanungo","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2007.01.003","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations","volume":"72","author":"Yalcin","year":"2008","journal-title":"Catena"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2011.04.012","article-title":"An easy-to-use Matlab program (Mamland) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm","volume":"38","author":"Akgun","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.jseaes.2012.10.005","article-title":"Landslide susceptibility mapping at Golestan province, Iran: A comparison between frequency ratio, dempster\u2013shafer, and weights-of-evidence models","volume":"61","author":"Mohammady","year":"2012","journal-title":"J. Asian Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s002540100310","article-title":"Statistical analysis of landslide susceptibility at Yongin, Korea","volume":"40","author":"Lee","year":"2001","journal-title":"Environ. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Simons, M. (1962). The Morphological Analysis of Landforms: A New Review of the Work of Walther Penck (1888\u20131923), JSTOR.","DOI":"10.2307\/621083"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.catena.2013.08.006","article-title":"Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo river catchment (Northern Calabria, Italy)","volume":"113","author":"Conforti","year":"2014","journal-title":"Catena"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The varnes classification of landslide types, an update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jhydrol.2011.05.015","article-title":"Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)","volume":"405","author":"Ozdemir","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.enggeo.2005.07.011","article-title":"The 17 March 2005 Kuzulu Landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity","volume":"81","author":"Gokceoglu","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.2136\/sssaj1986.03615995005000050042x","article-title":"Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation","volume":"50","author":"Moore","year":"1986","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.cageo.2010.10.012","article-title":"Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area","volume":"37","author":"Oh","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2014.05.013","article-title":"Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (lidar) data at catchment scale","volume":"152","author":"Jebur","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/10106049.2016.1155658","article-title":"Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea","volume":"32","author":"Pradhan","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1016\/j.cageo.2006.02.005","article-title":"GIS modeling for predicting river runoff volume in ungauged drainages in the greater Toronto area, Canada","volume":"32","author":"Cheng","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/w6010032","article-title":"Evaluation of version-7 TRMM multi-satellite precipitation analysis product during the Beijing extreme heavy rainfall event of 21 July 2012","volume":"6","author":"Huang","year":"2013","journal-title":"Water"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s12665-012-1842-5","article-title":"Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea","volume":"68","author":"Park","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.catena.2015.05.019","article-title":"Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines","volume":"133","author":"Hong","year":"2015","journal-title":"Catena"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/02693799608902084","article-title":"Geographic information systems for geoscientists\u2014Modelling with GIS\u2014Bonhamcarter, GF","volume":"10","author":"Laxton","year":"1996","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley. [3rd ed.].","DOI":"10.1002\/9781118548387"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Djeddaoui, F., Chadli, M., Gloaguen, R., Djeddaoui, F., Chadli, M., and Gloaguen, R. (2017). Desertification susceptibility mapping using logistic regression analysis in the Djelfa area, Algeria. Remote Sens., 9.","DOI":"10.3390\/rs9101031"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1023\/A:1021193827501","article-title":"Conditional independence test for weights-of-evidence modeling","volume":"11","author":"Agterberg","year":"2002","journal-title":"Nat. Resour. Res."},{"key":"ref_53","first-page":"55","article-title":"Principal component analysis in meteorology and oceanography","volume":"17","author":"Preisendorfer","year":"1988","journal-title":"Dev. Atmos. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0169-555X(01)00087-3","article-title":"Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong","volume":"42","author":"Dai","year":"2002","journal-title":"Geomorphology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/cc3045","article-title":"Statistics review 14: Logistic regression","volume":"9","author":"Bewick","year":"2005","journal-title":"Crit. Care"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10346-012-0380-2","article-title":"A comparison of logistic regression-based models of susceptibility to landslides in Western Colorado, USA","volume":"11","author":"Regmi","year":"2014","journal-title":"Landslides"},{"key":"ref_57","unstructured":"Clark, W., and Hosking, P. (1986). Statistical Methods for Geographers, John Wiley & Sons."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10346-008-0138-z","article-title":"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","volume":"6","author":"Mathew","year":"2009","journal-title":"Landslides"},{"key":"ref_59","first-page":"1789","article-title":"Landslide susceptibility mapping in Mawat area, Kurdistan Region, NE Iraq: A comparison of different statistical models","volume":"3","author":"Othman","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2000). Model-building strategies and methods for logistic regression. Applied Logistic Regression, Wiley. [3rd ed.].","DOI":"10.1002\/0471722146"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.3390\/rs3081568","article-title":"Identification of mangrove areas by remote sensing: The ROC curve technique applied to the northwestern Mexico coastal zone using Landsat imagery","volume":"3","author":"Alatorre","year":"2011","journal-title":"Remote Sens."},{"key":"ref_64","unstructured":"Chen, Y., and Booth, D.C. (2011). The Wenchuan Earthquake of 2008: Anatomy of a Disaster, Springer Science & Business Media."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.tecto.2014.06.016","article-title":"Present day crustal vertical movement inferred from precise leveling data in eastern margin of Tibetan plateau","volume":"632","author":"Hao","year":"2014","journal-title":"Tectonophysics"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/379505a0","article-title":"Bedrock incision, rock uplift and threshold hillslopes in the northwestern Himalayas","volume":"379","author":"Burbank","year":"1996","journal-title":"Nature"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s11069-014-1065-z","article-title":"Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeok-ri Creek, South Korea","volume":"72","author":"Pradhan","year":"2014","journal-title":"Nat. Hazards"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:28:45Z","timestamp":1760196525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,8]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["ijgi7110438"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7110438","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,8]]}}}