A novel model for regional susceptibility mapping of rainfall-reservoir induced landslides in Jurassic slide-prone strata of western Hubei Province, Three Gorges Reservoir area

Springer Science and Business Media LLC - Tập 35 - Trang 1403-1426 - 2020
Jingjing Long1, Yong Liu2, Changdong Li1, Zhiyong Fu1, Haikuan Zhang1
1Faculty of Engineering, China University of Geosciences, Wuhan, China
2School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, China

Tóm tắt

Jurassic facility-sliding strata have been identified as a fundamental factor affecting the occurrence of rainfall-reservoir induced landslides in western Hubei Province, China Three Gorges Reservoir area. Regional landslide susceptibility mapping is the most effective method for landslide prediction and mitigation. To solve the current problem of identifying the true landslides and non-landslides, a novel hybrid model based on the two steps self-organizing mapping-random forest (two steps SOM-RF) algorithm is proposed. The identified high and very high susceptibility zones are located within the hydro-fluctuation belt and regions with low altitude based on the datasets before 2014. Deviation and variance of other ten datasets are generated to evaluate the reliability of the maps for the problem of unbalanced sample sizes. Two typical landslides occurred in 2017 have been found in the very high susceptibility zone, which emphasized the validity of susceptibility mapping. To verify the effectiveness of selecting true landslides and non-landslides based on two steps SOM model, recorded landslides and non-landslides randomly chosen from landslide-free areas are put into the single RF model for comparison. The receiver operating characteristic curve and Accuracy index are applied to compare the performance of the landslide susceptibility maps created based on two steps SOM-RF and the single RF model. The results demonstrate that the consideration of true landslides and non-landslides is effective in producing a more accurate landslide susceptibility map with superior prediction skill and higher reliability.

Tài liệu tham khảo

Abbaszadeh Shahri A, Spross J, Johansson F, Larsson S (2019) Landslide susceptibility hazard map in southwest Sweden using artificial neural network. CATENA. https://doi.org/10.1016/j.catena.2019.104225 Althuwaynee O, Pradhan B, Park H, Lee J (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36. https://doi.org/10.1016/j.catena.2013.10.011 Atkinson P, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 130:55–64. https://doi.org/10.1016/j.geomorph.2011.02.001 Bai S, Lü G, Wang J, Zhou P, Ding L (2010a) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62:139–149. https://doi.org/10.1007/s12665-010-0509-3 Bai S, Wang J, Lü G, Zhou P, Hou S, Xu S (2010b) 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 Breiman L (2001) Random forest. Mach Learn 45:5–32 Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard Earth Sys 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013 Chen W, Chai H, Zhao Z, Wang Q, Hong H (2016) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environ Earth Sci. https://doi.org/10.1007/s12665-015-5093-0 Chen T, Zhu L, Niu R, Trinder C, Peng L, Lei T (2020) Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J Mt Sci 17:670–685. https://doi.org/10.1007/s11629-019-5839-3 Choi J, Oh H, Won J, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci 60:473–483. https://doi.org/10.1007/s12665-009-0188-0 Choi J, Oh H, Lee H, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011 Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, van DenEeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (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 Deng Q, Fu M, Ren X, Liu F, Tang H (2017) Precedent long-term gravitational deformation of large-scale landslides in the Three Gorges reservoir area, China. Eng Geol 221:170–183. https://doi.org/10.1016/j.enggeo.2017.02.017 Dou J, Yunus A, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen C, Khosravi K, Yang Y, Thai Pham B (2019) Assessment of advanced random forest and decision tree algorithms for modelling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island. Jpn Sci Total Environ 662:332–346. https://doi.org/10.1007/s10346-019-01286-5 Dou J, Yunus A, Bui D, Merghadi A, Sahana M, Zhu Z, Chen C, Han Z, Pham B (2020a) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17:641–658. https://doi.org/10.1007/s10346-019-01286-5 Dou J, Yunus A, Merghadi A, Shirzadi A, Nguyen H, Hussain Y, Avtar R, Chen Y, Pham B, Yamagishi H (2020b) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 720:137320. https://doi.org/10.1016/j.scitotenv.2020.137320 Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102:99–111. https://doi.org/10.1016/j.enggeo.2008.03.014 Godt J, Baum R, Savage W, Salciarini D, Schulz W, Harp E (2008) Transient deterministic shallow landslide modelling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol 102:214–226. https://doi.org/10.1016/j.enggeo.2008.03.019 Gómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27. https://doi.org/10.1016/j.enggeo.2004.10.004 Guzzetti A (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study. Cent Italy Geomorphol 31:181–216 Guzzetti F, Mondini A, Cardinali M, Fiorucci F, Santangelo M, Chang K (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 He S, Pan P, Dai L, Wang H, Liu J (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171–172:30–41. https://doi.org/10.1016/j.geomorph.2012.04.024 Hong H, Pourghasemi H, Pourtaghi Z (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118. https://doi.org/10.1016/j.geomorph.2016.02.012 Huang F, Yin K, Huang J, Gui L, Wang P (2017a) 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 Huang H, Long J, Lin H, Zhang L, Yi W, Lei B (2017b) Unmanned aerial vehicle based remote sensing method for monitoring a steep mountainous slope in the Three Gorges Reservoir, China. Earth Sci Inform 10:287–301. https://doi.org/10.1007/s12145-017-0291-9 Huang H, Song K, Yi W, Long J, Liu Q, Zhang G (2018) Use of multi-source remote sensing images to describe the sudden Shanshucao landslide in the Three Gorges Reservoir, China. Bull Eng Geol Environ 78:2591–2610. https://doi.org/10.1007/s10064-018-1261-2 Huang F, Cao Z, Guo J, Jiang S, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA. https://doi.org/10.1016/j.catena.2020.104580 Jaafari A, Najafi A, Pourghasemi H, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926. https://doi.org/10.1007/s13762-013-0464-0 Kavzoglu T, Sahin E, Colkesen I (2013) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439. https://doi.org/10.1007/s10346-013-0391-7 Kayastha P, Dhital M, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408. https://doi.org/10.1016/j.cageo.2012.11.003 Kim H, Lee D, Park C, Ahn Y, Kil S, Sung S, Biging GS (2018) Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods. Stoch Environ Res Risk Assess 32(11):2987–3019. https://doi.org/10.1007/s00477-018-1609-y Kohonen T (1982) Self-organised formation of topologically correct feature map. Biol Cybern 43(1):59–69 Lee S, Pradhan B (2006) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y Lee C, Huang C, Lee J, Pan K, Lin M, Dong J (2008) Statistical approach to earthquake-induced landslide susceptibility. Eng Geol 100:43–58. https://doi.org/10.1016/j.enggeo.2008.03.004 Li J, Zhou C (2003) Appropriate grid size for terrain-based landslide risk assessment in Lantau island, Hong Kong. J Remote Sens (in Chinese) 7(2):86–92 Li C, Tang H, Ge Y, Hu X, Wang L (2014) Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China. Stoch Environ Res Risk Assess 28(6):1465–1477. https://doi.org/10.1007/s00477-014-0848-9 Li C, Yan J, Wu J, Lei G, Wang L, Zhang Y (2019a) Determination of the embedded length of stabilizing piles in colluvial landslides with upper hard and lower weak bedrock based on the deformation control principle. Bull Eng Geol Environ 78:1189–1208. https://doi.org/10.1007/s10064-017-1123-3 Li C, Fu Z, Wang Y, Tang H, Yan J, Gong W, Yao W, Criss Robert E (2019b) Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the three gorges reservoir area: zigui Basin as an example. Eng Geol. https://doi.org/10.1016/j.enggeo.2019.105279 Liu X (2006) Exploratory under-sampling for class-imbalance learning. International conference on data mining. IEEE Comput Soc. https://doi.org/10.1109/ICDM.2006.68 Liu C, Liu Y, Wen M, Li T, Lian J, Qin S (2009) Landslide disaster mitigation in three gorges reservoir, China. Environ Sci Eng 1:1. https://doi.org/10.1007/978-3-642-00132-1 Liu Z, Guo D, Lacasse S, Li J, Yang B, Choi J (2020) Algorithms for intelligent prediction of landslide displacements. J Zhejiang Univ SC A 21:412–429. https://doi.org/10.1631/jzus.A2000005 Merghadi A et al (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103225 Mokhtari M, Abedian S (2019) Spatial prediction of landslide susceptibility in Taleghan basin, Iran. Stoch Environ Res Risk Assess 33(7):1297–1325. https://doi.org/10.1007/s00477-019-01696-w Nefeslioglu H, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191. https://doi.org/10.1016/j.enggeo.2008.01.004 Nguyen V et al (2019) Hybrid machine learning approaches for landslide susceptibility modeling. Forests. https://doi.org/10.3390/f10020157 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. https://doi.org/10.1016/j.jseaes.2012.12.014 Ozer B, Mutlu B, Nefeslioglu H, Sezer A, Rouai M, Dekayir A, Gokceoglu C (2019) On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco). Bull Eng Geol Environ. https://doi.org/10.1007/s10064-019-01548-5 Park S, Choi C, Kim B, Kim J (2012) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464. https://doi.org/10.1007/s12665-012-1842-5 Park H, Lee J, Woo I (2013) Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach. Eng Geol 161:1–15. https://doi.org/10.1016/j.enggeo.2013.04.011 Park H, Jang J, Lee J (2019) Assessment of rainfall-induced landslide susceptibility at the regional scale using a physically based model and fuzzy-based Monte Carlo simulation. Landslides 16:695–713. https://doi.org/10.1007/s10346-018-01125-z Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301. https://doi.org/10.1016/j.geomorph.2013.08.013 Pham B, Tien Bui D, Prakash I, Dholakia M (2017) 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 Pourghasemi H, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192. https://doi.org/10.1016/j.catena.2017.11.022 Pourghasemi H, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996. https://doi.org/10.1007/s11069-012-0217-2 Pourghasemi H, Teimoori Yansari Z, Panagos P, Pradhan B (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016) Arabian. J Geosci. https://doi.org/10.1007/s12517-018-3531-5 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 Pradhan B, Oh H, Buchroithner M (2010a) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area Geomatics. Nat Hazards Risk 1:199–223. https://doi.org/10.1080/19475705.2010.498151 Pradhan B, Lee S, Buchroithner M (2010b) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235. https://doi.org/10.1016/j.compenvurbsys.2009.12.004 Regmi A, Devkota K, Yoshida K, Pradhan B, Pourghasemi H, Kumamoto T, Akgun A (2013) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742. https://doi.org/10.1007/s12517-012-0807-z Reis S, Yalcin A, Atasoy M, Nisanci R, Bayrak T, Erduran M, Sancar C, Ekercin S (2011) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ Earth Sci 66:2063–2073. https://doi.org/10.1007/s12665-011-1432-y Sevgen E, Kocaman S, Nefeslioglu H, Gokceoglu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors 19(18):3940. https://doi.org/10.3390/s19183940 Sharma S, Mahajan A (2018) A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull Eng Geol Environ 78:2431–2448. https://doi.org/10.1007/s10064-018-1259-9 Solaimani K, Mousavi S, Kavian A (2012) Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab J Geosci 6:2557–2569. https://doi.org/10.1007/s12517-012-0526-5 Tang H, Wasowski J, Juang CH (2019) Geohazards in the three Gorges Reservoir Area, China—lessons learned from decades of research. Eng Geol. https://doi.org/10.1016/j.enggeo.2019.105267 Tien BD, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) 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 Torizin J (2016) Elimination of informational redundancy in the weight of evidence method: an application to landslide susceptibility assessment. Stoch Environ Res Risk Assess 30(2):635–651. https://doi.org/10.1007/s00477-015-1077-6 Wang H, Sassa K (2005) Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environ Geol 47:956–966. https://doi.org/10.1007/s00254-005-1225-2 Wolpert D, Macready W (1999) An efficient method to estimate bagging’s generalization error. Mach Learn 35(1):41–55. https://doi.org/10.1023/A:1007519102914 Wu C, Chen S (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112:190–204. https://doi.org/10.1016/j.geomorph.2009.06.002 Xu G, Li W, Yu Z, Ma X, Yu Z (2015) The 2 September 2014 Shanshucao landslide, Three Gorges Reservoir, China. Landslides 12:1169–1178. https://doi.org/10.1007/s10346-015-0652-8 Yan Y, Cui Y, Tian X, Hu S, Liao L (2020a) Seismic signal recognition and interpretation of the 2019 “7.23” Shuicheng landslide by seismogram stations. Landslides. https://doi.org/10.1007/s10346-020-01358-x Yan Y, Cui Y, Guo J, Hu S, Yin S (2020b) Landslide reconstruction using seismic signal characteristics and numerical simulations: case study of the 2017 “6.24” Xinmo landslide. Eng Geol. https://doi.org/10.1016/j.enggeo.2020.105582 Yanar T, Kocaman S, Gokceoglu C (2020) Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS Int J Geoinf 9(2):114. https://doi.org/10.3390/ijgi9020114 Yao W, Li C, Zuo Q, Zhan H, Criss Robert E (2019) Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China. Geomorphology 343:34–47. https://doi.org/10.1016/j.geomorph.2019.06.024 Yao W, Li C, Zhan H, Zhou J, Jiang X (2020a) Multiscale study of physical and mechanical properties of sandstone in Three Gorges Reservoir region subjected to cyclic wetting–drying of Yangtze river water. Rock Mec Rock Eng. https://doi.org/10.1007/s00603-019-02037-7 Yao W, Li C, Zhan H, Zhang H, Chen W (2020b) Probabilistic multi-objective optimization for landslide reinforcement with stabilizing piles in Zigui Basin of Three Gorges Reservoir region, China. Stoch Environ Res Risk Assess 34(6):807–824. https://doi.org/10.1007/s00477-020-01800-5 Yin Y, Huang B, Chen X, Liu G, Wang S (2015) Numerical analysis on wave generated by the Qianjiangping landslide in Three Gorges Reservoir, China. Landslides 12:355–364. https://doi.org/10.1007/s10346-015-0564-7 Yin Y, Huang B, Wang W, Wei Y, Ma X, Ma F, Zhao C (2016) Reservoir-induced landslides and risk control in Three Gorges Project on Yangtze River, China. J Rock Mech Geotech Eng 8:577–595. https://doi.org/10.1016/j.jrmge.2016.08.001 Youssef A, Al-Kathery M, Pradhan B (2014a) Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19:113–134. https://doi.org/10.1007/s12303-014-0032-8 Youssef A, Pradhan B, Jebur MN, El-Harbi HM (2014b) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73:3745–3761. https://doi.org/10.1007/s12665-014-3661-3 Zhu M, Tao X (2012) The SVM classifier for unbalanced data based on combination of RU-undersample and smote. Inf Technol (01), 39–43 (in Chinese) Zhou G, Esaki T, Mitani Y, Xie M, Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68(3–4):373–386. https://doi.org/10.1016/s0013-7952(02)00241-7 Zhu A, Wang R, Qiao J, Qin C, Chen Y, Liu J (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138. https://doi.org/10.1016/j.geomorph.2014.02.003 Zou Z, Yang Y, Fan Z, Tang H, Ma J (2020) Suitability of data preprocessing methods for landslide displacement forecasting. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-020-01824-x