{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:40:10Z","timestamp":1774942810863,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T00:00:00Z","timestamp":1497225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundations of China","award":["41572257"],"award-info":[{"award-number":["41572257"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.<\/jats:p>","DOI":"10.3390\/ijgi6060172","type":"journal-article","created":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T10:27:59Z","timestamp":1497263279000},"page":"172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8849-7298","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Peihua","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Changming","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"523","DOI":"10.3390\/ijgi3020523","article-title":"GIS supported landslide susceptibility modeling at regional scale: An expert-based fuzzy weighting method","volume":"3","author":"Chalkias","year":"2014","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-015-4950-1","article-title":"Random forests and evidential belief function-based landslide susceptibility assessment in western Mazandaran Province, Iran","volume":"75","author":"Pourghasemi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at selangor, malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"486","DOI":"10.4028\/www.scientific.net\/AMM.225.486","article-title":"Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon\u2019s entropy and GIS","volume":"225","author":"Pourghasemi","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"ref_6","unstructured":"Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I., and Dick, O.B. (2012, January 1\u20135). Application of support vector machines in landslide susceptibility assessment for the Hoa Binh Province (Vietnam) with kernel functions analysis. Proceedings of the iEMSs Sixth Biennial Meeting, Leipzig, Germany."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hou, W., Lu, X., Wu, P., Xue, A., and Li, L. (2017). An integrated approach for monitoring and information management of the Guanling Landslide (China). ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6030079"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3390\/ijgi4010400","article-title":"Manifestation of an analytic hierarchy process (AHP) model on fire potential zonation mapping in kathmandu metropolitan city, Nepal","volume":"4","author":"Chhetri","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11069-011-9844-2","article-title":"Landslide susceptibility analysis in the Hoa Binh Province of Vietnam using statistical index and logistic regression","volume":"59","author":"Bui","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s11069-013-0728-5","article-title":"Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances","volume":"69","author":"Pourghasemi","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s12517-012-0610-x","article-title":"Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms","volume":"6","author":"Zare","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s12040-013-0282-2","article-title":"Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran","volume":"122","author":"Pourghasemi","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H.R., and Rossi, M. (2016). Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: A comparison between GLM, GAM, MARS, and M-AHP methods. Theor. Appl. Climatol., 1\u201325.","DOI":"10.1007\/s00704-016-1919-2"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2013.05.010","article-title":"A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments","volume":"59","author":"Nefeslioglu","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_20","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_21","first-page":"131","article-title":"Hillslope profile analysis (comment)","volume":"10","author":"Cox","year":"1978","journal-title":"Area"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/ppp.3430010107","article-title":"Analysis of the segmentation in the profile of Alpine Talus Slopes","volume":"1","author":"Francou","year":"1990","journal-title":"Permafrost Periglac. Process."},{"key":"ref_23","unstructured":"Savigear, R. (1967). The Analysis and Classification of Slope Profile Forms, Universit\u00e9 de Li\u00e8ge."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/esp.3290160505","article-title":"GIS techniques and statistical models in evaluating landslide hazard","volume":"16","author":"Carrara","year":"1991","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_25","first-page":"172","article-title":"Uncertainty in assessing landslide hazard and risk","volume":"2","author":"Carrara","year":"1992","journal-title":"ITC J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Carrara, A., Cardinali, M., Guzzetti, F., and Reichenbach, P. (1995). GIS technology in mapping landslide hazard. Geographical Information Systems in Assessing Natural Hazards, Springer.","DOI":"10.1007\/978-94-015-8404-3"},{"key":"ref_27","first-page":"273","article-title":"Landslide inventory map of the Umbria Region, central Italy","volume":"90","author":"Guzzetti","year":"1990","journal-title":"Proc. ALPS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/BF02400865","article-title":"The avi project: A bibliographical and archive inventory of landslides and floods in Italy","volume":"18","author":"Guzzetti","year":"1994","journal-title":"Environ. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"531","DOI":"10.2113\/gseegeosci.II.4.531","article-title":"The influence of structural setting and lithology on landslide type and pattern","volume":"2","author":"Guzzetti","year":"1996","journal-title":"Environ. Eng. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0094-5765(87)90017-8","article-title":"The automatic generation of digital terrain models from satellite images by stereo","volume":"15","author":"Cooper","year":"1987","journal-title":"Acta Astronaut."},{"key":"ref_31","unstructured":"Ehlers, M., and Welch, R. (1987). Stereocorrelation of Landsat TM Images, Food and Agriculture Organization of the United Nations."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compgeo.2012.04.007","article-title":"Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area","volume":"45","author":"Jia","year":"2012","journal-title":"Comput. Geotech."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0169-555X(97)00064-0","article-title":"An automated approach to the classification of the slope units using digital data","volume":"21","author":"Giles","year":"1998","journal-title":"Geomorphology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0378-4754(78)90064-2","article-title":"Modeling unstructured decision problems\u2014The theory of analytical hierarchies","volume":"20","author":"Saaty","year":"1978","journal-title":"Math. Comput. Simul."},{"key":"ref_35","first-page":"324","article-title":"Hazard evaluation of secondary geological disaster based on GIS and information value method","volume":"35","author":"Du","year":"2010","journal-title":"Earth Sci. Diqiu Kexue"},{"key":"ref_36","first-page":"991","article-title":"Landslide hazard evaluation of wanzhou based on GIS information value method in the Three Gorges Reservoir","volume":"25","author":"Gao","year":"2006","journal-title":"Yanshilixue Yu Gongcheng Xuebao"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1139\/l92-092","article-title":"Breach of a naturally embanked dam on Yalong River","volume":"19","author":"Chen","year":"1992","journal-title":"Can. J. Civ. Eng."},{"key":"ref_38","unstructured":"Li, T., Schuster, R.L., and Wu, J. (1986). Landslide dams in south-central China. Landslide Dams: Processes, Risk, and Mitigation, ASCE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0169-555X(02)00358-6","article-title":"A super-large landslide in Tibet in 2000: Background, occurrence, disaster, and origin","volume":"54","author":"Shang","year":"2003","journal-title":"Geomorphology"},{"key":"ref_40","first-page":"1206","article-title":"Analysis of landslide influential factors and coupling intensity based on third theory of quantification","volume":"6","author":"Junxia","year":"2010","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s12524-010-0020-z","article-title":"Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches","volume":"38","author":"Pradhan","year":"2010","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_43","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_44","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_45","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_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/B:NHAZ.0000026786.85589.4a","article-title":"Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques","volume":"32","author":"Ercanoglu","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.geomorph.2006.10.036","article-title":"Landslide susceptibility mapping for a part of tectonic kelkit valley (eastern black sea region of Turkey)","volume":"94","author":"Nefeslioglu","year":"2008","journal-title":"Geomorphology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1007\/s12665-011-1196-4","article-title":"GIS-based landslide susceptibility mapping using bivariate statistical analysis in devrek (zonguldak-Turkey)","volume":"65","author":"Yilmaz","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1007\/s11069-012-0463-3","article-title":"GIS-multicriteria decision analysis for landslide susceptibility mapping: Comparing three methods for the Urmia Lake Basin, Iran","volume":"65","author":"Feizizadeh","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s11069-012-0414-z","article-title":"Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China)","volume":"65","author":"Xu","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11629-016-4126-9","article-title":"Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China","volume":"14","author":"Du","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_52","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_53","unstructured":"Guzzetti, F. (1993, January 20\u201322). Landslide hazard and risk by GIS-based multivariate models. Proceedings of the International Workshop GIS in Assessing Natural Hazards, Perugia, Italy."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s12665-016-6365-z","article-title":"The 102 landslide: Human-slope interaction in SE Tibet over a 20-year period","volume":"76","author":"Shang","year":"2016","journal-title":"Environ. Earth Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/6\/172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:38:46Z","timestamp":1760207926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/6\/172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,12]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["ijgi6060172"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6060172","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,12]]}}}