Abstract
The objective of this study was to produce a landslide susceptibility map along the rapidly uplifting section of the upper Jinsha River. Firstly, a total of 40 landslides were identified in the study area from the interpretation of remote sensing (RS) and field survey data. Following landslide identification, ten variables including slope angle, slope aspect, curvature, land use, normalised difference vegetation index (NDVI), rainfall, lithology, distance to river, distance to fault, and Strahler’s integral value were selected as the influencing factors in landslide susceptibility mapping. All of the influencing factors were extracted by the slope unit. The Strahler’s integral value was used to represent the relationship between the rate of uplift and rate of denudation in each slope unit. Furthermore, three methods, including logistic regression, a support vector machine, and an artificial neural network, were applied to landslide susceptibility modelling. Five-fold cross validation, a statistical analysis method, and the area under the receiver operating characteristic curve (AUC) were used to compare the evaluation results of the three models. Finally, the variance-based method was used to find the key factors associated with landslides in the study area. The results show that the mean prediction accuracies of the logistic regression model, artificial neural network model, and support vector machine model were 80.47%, 87.30%, and 83.94% in the training stage, respectively, and 81.08%, 82.16%, and 83.51% in the validating stage, respectively. The mean AUCs of the three models were 88.16%, 93.96%, and 89.68% in the training stage, respectively, and 87.68%, 92.60%, and 89.88% in the validating stage, respectively. These results show that the artificial neural network model is the best model for evaluating landslide susceptibility in this study. The landslide susceptibility map produced by the artificial neural network model was divided into five classes, including very low, low, moderate, high, and very high, and the percentages of the areas of the five susceptibility classes were 17.23%, 28.32%, 22.73%, 16.73%, and 15.00%, respectively. Furthermore, the distance to river, slope aspect, lithology, and distance to fault are the most important influencing factors for landslide susceptibility mapping in the study area. Consequently, this study will be a useful guide for landslide prevention, mitigation, and future land planning in the study area.
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References
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Bai S-B, Wang J, Lue G-N, Zhou P-G, Hou S-S, Xu S-N (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three gorges area, China. Geomorphology 115:23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
Ballabio C, Sterlacchini S (2012) Support vector Machines for Landslide Susceptibility Mapping: the Staffora River basin case study, Italy. Math Geosci 44:47–70. https://doi.org/10.1007/s11004-011-9379-9
Binh Thai P, Dieu Tien B, Dholakia MB, Prakash I, Ha Viet P, Mehmood K, Hung Quoc L (2017a) A novel ensemble classifier of rotation forest and naive Bayer for landslide susceptibility assessment at the Luc yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics Nat Hazards Risk 8:649–671. https://doi.org/10.1080/19475705.2016.1255667
Binh Thai P, Dieu Tien B, Pourghasemi HR, Indra P, Dholakia MB (2017b) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naive bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273. https://doi.org/10.1007/s00704-015-1702-9
Binh Thai P, Dieu Tien B, Prakash I, Dholakia MB (2017c) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using. GIS Catena 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007
Bui DT, Tsangaratos P, Ngo PT, Pham TD, Pham BT (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054. https://doi.org/10.1016/j.scitotenv.2019.02.422
Burbank DW, Leland J, Fielding E, Anderson RS, Brozovic N, Reid MR (1996) Bedrock incision, rock uplift and threshold hillslopes in the northwestern Himalayas. Nature 379(6565):505–510
Cao C, Wang Q, Chen J, Ruan Y, Zheng L, Song S, Niu C (2016a) Landslide susceptibility mapping in vertical distribution law of precipitation area: case of the Xulong Hydropower Station reservoir, southwestern China Water 8 https://doi.org/10.3390/w8070270
Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016b) Flash flood Hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas Sustainability 8 https://doi.org/10.3390/su8090948
Chen W et al (2017c) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017a) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327. https://doi.org/10.1016/j.geoderma.2017.06.020
Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017b) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85. https://doi.org/10.1016/j.geomorph.2017.09.007
Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250. https://doi.org/10.1016/j.catena2013.08.006
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324. https://doi.org/10.1007/s00254-007-0818-3
Davis WM (1906) The geographical cycle in an arid climate. Geogr J 27:70–73. https://doi.org/10.2307/1776794
Devkota KC et al (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165. https://doi.org/10.1007/s11069-012-0347-6
Dieu Tien B, Tran Anh T, Klempe H, Pradhan B, Revhaug I (2016) 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. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Ehlers M, Welch R (1987) Stereocorrelation of LANDSAT TM images. Photogramm Eng Remote Sens 53(9):1231–1237
Giles PT, Franklin SE (1998) An automated approach to the classification of the slope units using digital data. Geomorphology 21(3–4):251–264
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Hao M, Wang Q, Shen Z, Cui D, Ji L, Li Y, Qin S (2014) Present day crustal vertical movement inferred from precise leveling data in eastern margin of Tibetan plateau. Tectonophysics 632:281–292. https://doi.org/10.1016/j.tecto.2014.06.016
Harris N (2006) The elevation history of the Tibetan plateau and its implications for the Asian monsoon. Palaeogeogr Palaeoclimatol Palaeoecol 241:4–15. https://doi.org/10.1016/j.palaeo.2006.07.009
Hong H et al (2017) A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). Environ Earth Sci 76. https://doi.org/10.1007/s12665-017-6981-2
Hong H, Pradhan B, Jebur MN, Bui DT, Xu C, Akgun A (2016) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75. https://doi.org/10.1007/s12665-015-4866-9
Hong H, Pradhan B, Xu C, Tien Bui D (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019
Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Xu C (2018) Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci Total Environ 630:1044–1056. https://doi.org/10.1016/j.scitotenv.2018.02.278
Hosmer Jr DW, Lemeshow S, Sturdivant RX (2000) Model-building strategies and methods for logistic regression. In: Applied logistic regression, 3rd edn. Wiley, Hoboken: 89–151
Huang F, Yin K, Huang J, Gui L, Wang P (2017) Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol 223:11–22. https://doi.org/10.1016/j.enggeo.2017.04.013
Jia N, Mitani Y, Xie M, Djamaluddin I (2012) Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area. Comput Geotech 45:1–10. https://doi.org/10.1016/j.compgeo.2012.04.007
Jiang P, Chen J (2016) Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing 198:40–47. https://doi.org/10.1016/j.neucom.2015.08.118
Jiang P, Zeng Z, Chen J, Huang T (2014) Generalized regression neural networks with K-fold cross-validation for displacement of landslide forecasting. In: Zeng Z, Li Y, King I (eds) Advances in neural networks - Isnn 2014, vol 8866. Lecture notes in computer science: 533–541. https://doi.org/10.1007/978-3-319-12436-0_59
Jiang T, Lei P, Qin Q (2016) An application of SVM-based classification in landslide stability. Intell Auto Soft Comput 22:267–271. https://doi.org/10.1080/10798587.2015.1095480
Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11:352–359. https://doi.org/10.1016/j.jag.2009.06.002
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. https://doi.org/10.1016/j.geomorph.2017.06.013
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26:1477–1491. https://doi.org/10.1080/01431160412331331012
Li J, Zhou S, Zhao Z, Zhang J (2015) The Qingzang movement: the major uplift of the Qinghai-Tibetan plateau. Sci China-Earth Sci 58:2113–2122. https://doi.org/10.1007/s11430-015-5124-4
Li JJ, Fang XM (1999) Uplift of the Tibetan plateau and environmental changes. Chin Sci Bull 44:2117–2124. https://doi.org/10.1007/bf03182692
Mansouri A, Nazari A, Ramazani M (2016) A comparison of artificial neural network model and logistics regression in prediction of companies’ bankruptcy (a case study of Tehran stock exchange). Int J Adv Comput Res 6(24):81–92
McCulloch A (2005) Sensitivity analysis in practice: a guide to assessing scientific models. J Royal Stat Soc Ser a-Stat Soc 168:466–466. https://doi.org/10.1111/j.1467-985X.2005.358_16.x
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas, USA. Eng Geol 69:331–343. https://doi.org/10.1016/s0013-7952(03)00069-3
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran Environ Earth Sci 75 https://doi.org/10.1007/s12665-015-4950-1
Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon's entropy and GIS. In: Varatharajoo R, Abdullah EJ, Majid DL, Romli FI, Rafie ASM, Ahmad KA (eds) Aerotech iv: recent advances in aerospace technologies. Appl Mech Mater 225:486–48+. https://doi.org/10.4028/www.scientific.net/AMM.225.486
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. https://doi.org/10.1016/j.envsoft.2009.10.016
Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114:129–142. https://doi.org/10.1016/j.geomorph.2009.06.020
Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61:143–153. https://doi.org/10.1007/s11069-011-9846-0
Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145:280–297. https://doi.org/10.1016/s0010-4655(02)00280-1
Scheidegger AE (1979) The principle of antagonism in the earth's evolution. Tectonophysics 55(3):T7–T10
Schlogel R, Marchesini I, Alvioli M, Reichenbach P, Rossi M, Malet JP (2018) Optimizing landslide susceptibility zonation: effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology 301:10–20. https://doi.org/10.1016/j.geomorph.2017.10.018
Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Bull Geol Soc Amer 63
Su Q, Zhang J, Zhao S, Wang L, Liu J, Guo J (2017) Comparative assessment of three nonlinear approaches for landslide susceptibility mapping in a coal mine area Isprs International Journal of Geo-Information 6. https://doi.org/10.3390/ijgi6070228
Sun X, Chen J, Bao Y, Han X, Zhan J, Peng W (2018) Landslide susceptibility mapping using logistic regression analysis along the Jinsha River and its tributaries close to Derong and Deqin County, southwestern China Isprs International Journal of Geo-Information 7 https://doi.org/10.3390/ijgi7110438
Tian Y, Xiao C, Wu L (2010) Slope unit-based landslide susceptibility zonation. International conference on Geoinformatics. IEEE
Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74:1489–1516. https://doi.org/10.1007/s11069-014-1245-x
Xiao XC, Wang J (1998) A brief review of tectonic evolution and uplift of the Qinghai-Tibet plateau. Geol Rev 44:372–382
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014
Yang X, Chen L (2010) Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. Int J Appl Earth Obs Geoinf 12:487–495. https://doi.org/10.1016/j.jag.2010.05.006
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582. https://doi.org/10.1016/j.geomorph.2008.02.011
Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266. https://doi.org/10.1016/j.enggeo.2005.02.002
Zhan J, Chen J, Zhang W, Han X, Sun X, Bao Y (2018) Mass movements along a rapidly uplifting river valley: an example from the upper Jinsha River, southeast margin of the Tibetan plateau Environ Earth Sci 77 https://doi.org/10.1007/s12665-018-7825-4
Zhang DF, Fengquan L, Jianmin B (2000) Eco-environmental effects of the Qinghai-Tibet plateau uplift during the quaternary in China. Environ Geol 39:1352–1358. https://doi.org/10.1007/s002540000174
Acknowledgements
This research was financially supported by the Key Project of NSFC-Yunnan Joint Fund (grant no. U1702241), the State Key Program of National Natural Science Foundation of China (grant no. 41330636), the National Natural Science Foundation of China (grant no. 41807227), the China Postdoctoral Science Foundation Funded Project (grant no. 2017 M621212), and the Graduate Innovation Fund of Jilin University (grant no. 2017137). The authors would like to thank the editor and anonymous reviewers for their comments and suggestions which helped a lot in making this paper better.
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Sun, X., Chen, J., Han, X. et al. Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China. Bull Eng Geol Environ 79, 533–549 (2020). https://doi.org/10.1007/s10064-019-01572-5
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DOI: https://doi.org/10.1007/s10064-019-01572-5