Abstract
Landslide susceptibility assessment is highly valuable for disaster prevention and mitigation. This study utilized the aspects of data and content to comprehensively examine the research status of landslide susceptibility. First, we used CiteSpace to visually analyze papers with “landslide susceptibility” as their theme word in the Web of Science database from 1991 to 2020. Next, we summarized the characteristics of quantitative trends, journals, authors, organization types, and keywords, and created a map of authors, institutions, and keywords. Then we presented the common methods and their advantages and disadvantages of landslide inventory, evaluation index, evaluation unit, evaluation model and verification method, combs the shortcomings of each part at the present stage, and looks forward to the possible research direction in the future. Finally, the research difficulties of landslide susceptibility in spatial scale, qualitative and quantitative problems, and spatial representation of landslide information are discussed. We find that the development of 3S and new technology in computer field promotes the development of landslide susceptibility research, makes landslide inventory more efficient and accurate, makes the assessment factor system considering factor contribution more reasonable, and makes more intelligent models applied to landslide susceptibility research. The results are beneficial for researchers to understand the current landslide susceptibility condition and can provide a reference for subsequent landslide susceptibility studies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
References
Osako LS (2021) Updating landslide inventory maps using high resolution digital orthophotos and digital surface and elevation modeling: the case study of Brusque city, Santa Catarina state, Brazil. Remote Sens Spatial Inf Sci V-3-2021, 251–255. https://doi.org/10.5194/isprs-annals-V-3-2021-251-2021
Abedini M, Ghasemyan B, Rezaei Mogaddam MH (2017) Landslide susceptibility mapping in Bijar city, Kurdistan Province, Iran: a comparative study by logistic regression and AHP models. Environ Earth Sci 76:308. https://doi.org/10.1007/s12665-017-6502-3
Ai L, Yang BY, Guo L (2020) Comparison of landslide susceptibility evaluation based on different data units and models. Geospatial Inf 18:117–121
Althuwaynee OF, Aydda A, Hwang I-T et al (2021) Uncertainty reduction of unlabeled features in landslide inventory using machine learning t-SNE clustering and data mining apriori association rule algorithms. Appl Sci 11(2):556. https://doi.org/10.3390/app11020556
Antonielli B, Mazzanti P, Rocca A et al (2019) A-DInSAR performance for updating landslide inventory in mountain areas: an example from Lombardy Region (Italy). Geosciences 9(9):364. https://doi.org/10.3390/geosciences9090364
Aslan G, Foumelis M, Raucoules D et al (2020) Landslide mapping and monitoring using Persistent Scatterer Interferometry (PSI) technique in the French Alps. Remote Sensing 12(8):1305. https://doi.org/10.3390/rs12081305
Bacha AS, Shafique M, van der Werff H (2018) Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, Northern Pakistan. J Mt Sci 15:1354–1370. https://doi.org/10.1007/s11629-017-4697-0
Baeza C, Lantada N, Moya J (2010) Influence of sample and terrain unit on landslide susceptibility assessment at La Pobla de Lillet, Eastern Pyrenees, Spain. Environ Earth Sci 60:155–167. https://doi.org/10.1007/s12665-009-0176-4
Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329. https://doi.org/10.1007/s11069-005-5182-6
Bostjančić I, Filipović M, Gulam V et al (2021) Regional-scale landslide susceptibility mapping using limited LiDAR-based landslide inventories for Sisak-Moslavina county. Croatia Sustain 13(8):4543. https://doi.org/10.3390/su13084543
Bunn MD, Leshchinsky BA, Olsen MJ et al (2019) A simplified, object-based framework for efficient landslide inventorying using LIDAR digital elevation model derivatives. Remote Sensing 11(3):303. https://doi.org/10.3390/rs11030303
Calvello M, Cascini L, Mastroianni S (2013) Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units. Geomorphology 182:33–48. https://doi.org/10.1016/j.geomorph.2012.10.026
Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75:1–21. https://doi.org/10.1007/s12665-015-5047-6
Carrara A, Cardinali M, Detti R et al (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Processes Landforms 16:427–445. https://doi.org/10.1002/esp.3290160505
Chen CM (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57:359–377. https://doi.org/10.1002/asi.20317
Chen W, Yan XS, Zhao Z et al (2019) Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, Naive Bayes and RBFNetwork models for the Long County area (China). Bull Eng Geol Environ 78:247–266. https://doi.org/10.1007/s10064-018-1256-z
Chen T, Zhong ZY, Niu RQ, et al. (2020) Mapping landslide susceptibility based on deep belief network. Geomat Inf Sci Wuhan Univ 45(11):1809–1817. https://doi.org/10.13203/j.whugis20190144
Chen CM (2004) Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci 101(suppl 1), 5303–5310. https://doi.org/10.1073/pnas.0307513100
Chiessi V, Toti S, Vitale V (2016) Landslide susceptibility assessment using conditional analysis and rare events logistics regression: a case-study in the Antrodoco area (Rieti, Italy). J Geosci Environ Prot 04:1–21. https://doi.org/10.4236/gep.2016.412001
Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Corominas J, Moya J (2010) Contribution of dendrochronology to the determination of magnitude–frequency relationships for landslides. Geomorphology 124(3–4):137–149. https://doi.org/10.1016/j.geomorph.2010.09.001
Corominas J, van Westen C, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Das G, Lepcha K (2019) Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India. SN Appl Sci 1:1453. https://doi.org/10.1007/s42452-019-1499-8
DeGraff JV (1978) Regional landslide evaluation: two Utah examples. Geo 2:203–214. https://doi.org/10.1007/BF02380486
Du GL, Zhang YS, Iqbal J et al (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14:249–268. https://doi.org/10.1007/s11629-016-4126-9
Fernández T, Pérez-García JL, Gómez-López JM et al (2021) Multitemporal landslide inventory and activity analysis by means of aerial photogrammetry and LiDAR techniques in an area of southern Spain. Remote Sensing 13(11):2110. https://doi.org/10.3390/rs13112110
Fleuchaus P, Blum P, Wilde M et al (2021) Retrospective evaluation of landslide susceptibility maps and review of validation practice. Environ Earth Sci 80:485. https://doi.org/10.1007/s12665-021-09770-9
Florsheim JL, Nichols A (2013) Landslide area probability density function statistics to assess historical landslide magnitude and frequency in coastal California. CATENA 109(5):129–138. https://doi.org/10.1016/j.catena.2013.04.005
Fobert M-A, Singhroy V, Spray JG (2021) InSAR monitoring of landslide activity in Dominica. Remote Sensing 13(4):815. https://doi.org/10.3390/rs13040815
Galli M, Ardizzone F, Cardinali M et al (2008) Comparing landslide inventory maps. Geomorphology 94(3–4):268–289. https://doi.org/10.1016/j.geomorph.2006.09.023
Gao B, Wang XY (2019) Risk zoning of landslide based on SINMAP model in Yan’an City. Bull Soil Water Conserv 39(03):211–216. https://doi.org/10.13961/j.cnki.stbctb.2019.03.035
Gao HX, He YK, Zhang B et al (2020) Regional slope reliability calculation based on infinite slope model and probability theory. Saf Environ Eng 27(06):58–67. https://doi.org/10.13578/j.cnki.issn.1671-1556.2020.06.009
Gazibara SB, Krka M, Arbanas SM (2019) Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia). J Maps 15(2):773–779. https://doi.org/10.1080/17445647.2019.1671906
Ge DQ, Dai KR, Guo ZC et al (2019) Early ldentification of serious geological hazards with integrated remote sensing technologies: thoughts and recommendations. Geom Inf Sci Wuhan Univ 44(07):949–956. https://doi.org/10.13203/j.whugis20190094
Gholami M, Ghachkanlu EN, Khosravi K, Pirasteh S (2019) Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. J Earth Syst Sci 128:42. https://doi.org/10.1007/s12040-018-1047-8
Gokceoglu (2012) Discussion on “Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS” by Choi et al (2012), Engineering Geology, 124, 12–23. Eng Geol 129–130:104–105.https://doi.org/10.1016/j.enggeo.2012.01.008
Guo P, Tian W, Li H et al (2020) Global characteristics and trends of research on construction dust: based on bibliometric and visualized analysis. Environ Sci Pollut Res 27:37773–37789. https://doi.org/10.1007/s11356-020-09723-y
Guru B, Veerappan R, Sangma F, Bera S (2017) Comparison of probabilistic and expert-based models in landslide susceptibility zonation mapping in part of Nilgiri District, Tamil nadu, India. Spat Inf Res 25:757–768. https://doi.org/10.1007/s41324-017-0143-1
Hu WH, Song LJ, Miao CG et al (2010) Research on methods for classification of earthquake stricken areas and sequencing of disaster degrees—taking M8.0 Wenchuan earthquake as an example. J Catastrophol 25:30–35. https://doi.org/10.3969/j.issn.1000-811X.2010.02.006
Huang QL, Chen W, Tang XB et al (2017) Study on the method of slope unit zoning in regional geo-hazards risk assessment. J Nat Disasters 26:157–164. https://doi.org/10.13577/j.jnd.2017.0518
Huang FM, Pan LH, Yao C, et al (2021) Landslide susceptibility prediction modelling based on semi-supervised machine learning. J Zhejiang Univ (Eng Sci), pp 1–9[2021–10–20].
Korzeniowska K, Pfeifer N, Landtwing S (2018) Mapping gullies, dunes, lava fields, and landslides via surface roughness. Geomorphology 301:53–67. https://doi.org/10.1016/j.geomorph.2017.10.011
Kreuzer TM, Damm B (2020) Automated digital data acquisition for landslide inventories. Landslides 17:2205–2215. https://doi.org/10.1007/s10346-020-01431-5
Kubwimana D, Ait Brahim L, Nkurunziza P et al (2021) Characteristics and distribution of landslides in the populated hillslopes of Bujumbura. Burundi Geosci 11(6):259. https://doi.org/10.3390/geosciences11060259
Lan HX, Wang LJ, Zhou CH (2002) Study on GIS-AIDED model for analysis of landslide hazard. J Eng Geol 04:421–427. https://doi.org/10.3969/j.issn.1004-9665.2002.04.014
Li J, Zhou CH (2003) Appropriate grid size for terrain based landslide risk assessment in Lantau Island, Hong Kong. Natl Remote Sens Bull 02:86–92
Li YW, Wang XM, Mao H (2020) Influence of human activity on landslide susceptibility development in the Three Gorges area. Nat Hazards 104(prepublish):1–37. https://doi.org/10.1007/s11069-020-04264-6
Liang Z, Wang CM, Duan ZJ et al (2021) A hybrid model consisting of supervised and unsupervised learning for landslide susceptibility mapping. Remote Sensing 13(8):1464. https://doi.org/10.3390/rs13081464
Lima P, Steger S, Glade T (2021) Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria. Landslides 18:3531–3546. https://doi.org/10.1007/s10346-021-01693-7
Lin RF, Liu JP, Xu SH, et al (2020) Evaluation method of landslide susceptibility based on random forest weighted information. Sci Surv Map 45(12):131–138. https://doi.org/10.16251/j.cnki.1009-2307.2020.12.020
Luo SJ, Wang SS, Fu DQ (2021) Assessment on the susceptibility of sudden geological hazards in mountainous areas of Beijing. Chin J Geol Hazard Control 134(04):126–133. https://doi.org/10.16031/j.cnki.issn.1003-8035.2021.04-17
Luo W, Liu CC (2018) Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides 15:465–474. https://doi.org/10.1007/s10346-017-0893-9
Malamud BD, Turcotte DL, Guzzetti F et al (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29(6):687–711. https://doi.org/10.1002/esp.1064
Panek T, Brezny M, Kapustova V, et al. (2019) Large landslides and deep-seated gravitational slope deformations in the Czech Flysch Carpathians: new LiDAR-based inventory. Geomorphology 346:106852. https://doi.org/10.1016/j.geomorph.2019.106852
Paryani S, Neshat A, Javadi S et al (2020) GIS-based comparison of the GA-LR ensemble method and statistical models at Sefiedrood Basin. Iran Arab J Geosci 13:1029. https://doi.org/10.1007/s12517-020-06004-3
Peng DL, Xu Q, Dong XJ, et al. (2017) Accurate and efficient method for loess landslide fine mapping with high resolution close-range photogrammetry. J Eng Geol 25(02):424–435. https://doi.org/10.13544/j.cnki.jeg.2017.02.021
Pham BT, Tien Bui D, Prakash I et al (2017) A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ Earth Sci 76:371. https://doi.org/10.1007/s12665-017-6689-3
Pourghasemi HR, Teimoori Yansari Z, Panagos P et al (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11:193. https://doi.org/10.1007/s12517-018-3531-5
Qin XN, Lu XL, Wu CY (2014) The knowledge mapping of domestic ecological security research: bibliometric analysis based on citespace. Acta Ecol Sin 34:3693. https://doi.org/10.5846/stxb201211081566
Qiu JA, Liu XL (2017) Status and comprehensive analysis of benggang research in China based on knowledge maps. Sci Soil Water Conserv 15:139–148. https://doi.org/10.16843/j.sswc.2017.03.018
Raghuvanshi TK, Ibrahim J, Ayalew D (2014) Slope stability susceptibility evaluation parameter (SSEP) rating scheme-an approach for landslide hazard zonation. J Afr Earth Sci 99:595–612. https://doi.org/10.1016/j.jafrearsci.2014.05.004
Raja NB, Çiçek I, Türkoğlu N et al (2017) Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Nat Hazards 85:1323–1346. https://doi.org/10.1007/s11069-016-2591-7
Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76:499. https://doi.org/10.1007/s12665-017-6839-7
Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Rossi M, Guzzetti F, Reichenbach P et al (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114:129–142. https://doi.org/10.1016/j.geomorph.2009.06.020
Shi W, Deng S, Xu W (2018) Extraction of multi-scale landslide morphological features based on local Gi* using airborne LIDAR-derived DEM. Geomorphology 303:229–242. https://doi.org/10.1016/j.geomorph.2017.12.005
Shinoda M, Miyata Y, Kurokawa U, Kondo K (2019) Regional landslide susceptibility following the 2016 Kumamoto earthquake using back-calculated geomaterial strength parameters. Landslides 16:1497–1516. https://doi.org/10.1007/s10346-019-01171-1
Smith HG, Spiekermann R, Betts H et al (2021) Comparing methods of landslide data acquisition and susceptibility modelling: examples from New Zealand. Geomorphology 3:107660. https://doi.org/10.1016/j.geomorph.2021.107660
Su Z, Chow JK, Tan PS et al (2021) Deep convolutional neural network–based pixel-wise landslide inventory mapping. Landslides 18:1421–1443. https://doi.org/10.1007/s10346-020-01557-6
Sujatha ER (2012) Geoinformatics based landslide susceptibility mapping using probabilistic analysis and entropy index of Tevankarai stream sub-watershed. India Disaster Adv 5(3):26–33
Sujatha ER, Rajamanickam V (2011) Landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal taluk, India, using weighted similar choice fuzzy model. Nat Hazards 59:401–425. https://doi.org/10.1007/s11069-011-9763-2
Sujatha ER, Victor GR (2015) Landslide hazard and risk mapping using the weighted linear combination model applied to the Tevankarai Stream Watershed, Kodaikanal, India. Human Ecol Risk Assess Int J 21(6):1445–1461. https://doi.org/10.1080/10807039.2014.920222
Syzdykbayev M, Karimi B, Karimi HA (2020) Persistent homology on LiDAR data to detect landslides. Remote Sens Environ 246:111816. https://doi.org/10.1016/j.rse.2020.111816
Tang C, Ma GC (2015) Small regional geohazards susceptibility mapping based on geomorphic unit. Sci Geogr Sin 35:91–98. https://doi.org/10.13249/j.cnki.sgs.2015.01.011
Tang YM, Zhang MS, Li ZG, Feng W (2015) Review and comparison onInland and overseas geo-hazards risk management. Northwest Geol 48:238–246. https://doi.org/10.3969/j.issn.1009-6248.2015.02.025
Tanoli JI, Ningsheng C, Regmi AD, Jun L (2017) Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arab J Geosci 10:277. https://doi.org/10.1007/s12517-017-3026-9
Tian Y, Xu C, Ma S et al (2019b) Inventory and spatial distribution of landslides triggered by the 8th august 2017 MW 6.5 Jiuzhaigou earthquake. China Earth Sci 30:206–217. https://doi.org/10.1007/s12583-018-0869-2
Tian SJ, Zhang SS, Tang QS et al (2019) Comparative study of landslide susceptibility assessment based on different evaluation units. J Nat Disasters 28:137–145. https://doi.org/10.13577/j.jnd.2019.0615
Tsangaratos P, Loupasakis C, Nikolakopoulos K et al (2018) Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada. Greece Environ Earth Sci 77:363. https://doi.org/10.1007/s12665-018-7548-6
Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Wang K, Zhang SJ, Wei FQ (2019) Slope unit extraction methods: advances and prospects. J Yangtze River Sci Res Inst 37(6):85–93
Wei HM, He GX (2020) Literature characteristics and research hotspots of international urban underground utility tunnel research: visualized quantitative research based on CiteSpace. Tunnel Constr 40:179–188. https://doi.org/10.3973/j.issn.2096-4498.2020.02.004
Wei X, Zhang L, Luo J et al (2021) A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping. Nat Hazards 109:471–497. https://doi.org/10.1007/s11069-021-04844-0
Weidner L, Oommen T, Escobar-Wolf R et al (2018) Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions. Landslides 15:2343–2356. https://doi.org/10.1007/s10346-018-1044-7
Wu W, Xu C, Wang X et al (2020a) Landslides triggered by the 3 August 2014 Ludian (China) Mw 6.2 earthquake: an updated inventory and analysis of their spatial distribution. Earth Sci 31:853–866. https://doi.org/10.1007/s12583-020-1297-7
Wu WY, Xu C (2018) A new inventory of landslides triggered by the 2014 Ludian Mw6.2 earthquake. Seismol Geol 40(05):1140–1148.
Wu XL, Ren F, Niu RQ, et al (2013) Landslide spatial prediction based on slope units and support vector machines. Geom Inf Sci Wuhan Univ 38:1499–1503. https://doi.org/10.13203/j.whugis2013.12.006
Wu XL, Yang JY, Niu RQ (2020) A landslide susceptibility assessment method using SMOTE and convolutional neural network. Geomat Inf Sci Wuhan Univ 45(08):1223–1232. https://doi.org/10.13203/j.whugis20200127
Xie ZT, Chen G, Meng XM et al (2017) A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin. China Environ Earth Sci 76:313. https://doi.org/10.1007/s12665-017-6640-7
Xie CY (2011) A review of geological hazard spatial risk assessment. J Railway Sci Eng 8:97–102. https://doi.org/10.19713/j.cnki.43-1423/u.2011.01.019
Xing Y, Yue JP, Guo ZZ et al (2021) Large-scale landslide susceptibility mapping using an integrated machine learning model: a case study in the Lvliang Mountains of China. Front Earth Sci 9:622. https://doi.org/10.3389/feart.2021.722491
Xu C, Tian YY, Ma SY, et al. (2018) Inventory and spatial distribution of landslides in IX-XI high intensity areas of 1920 Haiyuan (China) M8.5 earthquake. J Eng Geol 26(05):1188–1195. https://doi.org/10.13544/j.cnki.jeg.2018110
Xu S, Wang SX, Niu RQ (2020a) ldentification of the potential landslide in Wushan—Fengjie in the three gorges reservoir area based on InSAR technology. Saf Environ Eng 27(01):32–38. https://doi.org/10.13578/j.cnki.issn.1671-1556.2020.01.006
Xu SH, Liu JP, Wang XH, et al (2020b) Landslide susceptibility assessment method incorporating index of entropy based on support vector machine: a case study of Shaanxi province. Geomat Inf Sci Wuhan Univ 45(08):1214–1222. https://doi.org/10.13203/j.whugis20200109
Yang C (2016) Assessing the landslide susceptibility of watershed units of Fujiian Province based on GIS. Dissertation. Fujian Normal University
Ye Z, Xu Q, Liu Q, et al. (2020) Application of unmanned aerial vehicle oblique photogrammetry to investigation of high slope rock structure. Geomat Inf Sci Wuhan Univ 45(11):1739–1746. https://doi.org/10.13203/j.whugis20200077
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 61:821–836. https://doi.org/10.1007/s12665-009-0394-9
Zhang Y, Meng X, Jordan C et al (2018) Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series. Landslides 15:1299–1315. https://doi.org/10.1007/s10346-018-0954-8
Zhang J, Yin KL, Wang JJ, et al (2016) Evaluation of landslide susceptibility for Wanzhou district of three gorges reservoir. Chin J Rock Mech Eng 35(2): 284–296. https://doi.org/10.13722/j.cnki.jrme.2015.0318
Zhang FY, Gu XB, Peng Y, Yuan T (2019) Analysis of knowledge map of disaster risk perception. Saf Environ Eng 26:32–37. https://doi.org/10.13578/j.cnki.issn.1671-1556.2019.02.005
Zhao JL, Wang JJ, Zhang J (2007) GIS technical application in geology disaster easy ammunition zone appraisement in Lingbao. Sci Technol Eng 11:2735–2740. https://doi.org/10.3969/j.issn.1671-1815.2007.11.071
Zhu JX, Zhang LZ, Zhou XY (2014) Characteristics of temporal scale of regional landslides susceptibility assessment. Soil Water Conserv China 6:18–21. https://doi.org/10.14123/j.cnki.swcc.2014.06.002
Acknowledgements
This work is supported by the Natural Science Research Project of Yichang City (No. A21-3-006), the China Institute of Geological Environment Monitoring Project (No. DD20190638), and the National Natural Science Foundation of China (No. 42107489).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yong, C., Jinlong, D., Fei, G. et al. Review of landslide susceptibility assessment based on knowledge mapping. Stoch Environ Res Risk Assess 36, 2399–2417 (2022). https://doi.org/10.1007/s00477-021-02165-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-021-02165-z