Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

The Qinghai–Tibet Plateau is an area with frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The primary purpose of this paper is to map the landslide susceptibility of the Ya’an–Lin branch of the Sichuan–Tibet Railway using two deep learning (DL) algorithms, convolutional neural network (CNN) and deep neural network (DNN). Initially, a geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology and field geological hazard surveys; thus, 18 landslide-influencing factors were selected. Subsequently, the landslides were randomly divided into training (70%) and validation data (30%) for model training and testing. Next, a Pearson correlation coefficient and information gain (IG) method were used to perform the correlation analysis and feature selection of the 18 influencing factors. Afterward, landslide susceptibility maps were generated for the two models. Finally, the performance of the model is validated using the receiver-operating characteristic (ROC) curve and confusion matrix. The results show that the CNN model (AUC = 0.88) provided better performance in both the training and testing phases compared to the DNN model (AUC = 0.84). In addition, the high landslide susceptibility is primarily distributed in the Jinsha, Lancang and Nu River basins along the railway. The slope, altitude and rainfall are the main factors for the formation of the landslides. Furthermore, the two deep learning models can accurately map the landslide susceptibility, providing important information for landslide risk reduction and prevention.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets that were generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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

    Article  Google Scholar 

  • 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(1/2):15–31

    Article  Google Scholar 

  • Azarafza M, Ghazifard A, Akgün H et al (2018) Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7978-1

    Article  Google Scholar 

  • Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11(1):1–16

    Article  Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters. Environ Earth Sci 66(2):537–548

    Article  Google Scholar 

  • Bui DT, Tsangaratos P, Nguyen VT, Liem NV, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426

    Article  Google Scholar 

  • Capitani M, Ribolini A, Federici PR (2013) Influence of deep-seated gravitational slope deformations on landslide distributions: a statistical approach. Geomorphology 201:127–134

    Article  Google Scholar 

  • Chang K, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9(12296):1603–1604

    Google Scholar 

  • Chen X, Chen W (2021) Gis-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena 196:104833

    Article  Google Scholar 

  • Chen W, Li Y (2020) Gis-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195:104777

    Article  Google Scholar 

  • Chen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014a) Landslide susceptibility mapping based on GIS and information value model for the Chencang district of Baoji, China. Arab J Geosci 7(11):4499–4511

    Article  Google Scholar 

  • Chen W, Li X, Wang Y, Chen G, Liu S (2014b) Forested landslide detection using lidar data and the random forest algorithm: a case study of the three gorges, China. Remote Sens Environ 152:291–301

    Article  Google Scholar 

  • Dahal R, Hasegawa S, Nonomura A et al (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102(3–4):496–510

    Article  Google Scholar 

  • Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1–4):131–156

    Article  Google Scholar 

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32(6):619–639

    Google Scholar 

  • Dong VD, Jaafari A, Bayat M et al (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA. https://doi.org/10.1016/j.catena.2019.104451

    Article  Google Scholar 

  • Dou J, Yunus AP, Merghadi A, Shirzadi A, Pham BT (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 10720:137320

    Article  Google Scholar 

  • Erener A, Sivas AA, Selcuk-Kestel AS, Düzgün HS (2017) Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods. Comput Geosci 104:62–74

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput Geosci 139:104470

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321–347

    Article  Google Scholar 

  • Girshick R (2015) Fast r-cnn. Comput Sci. https://doi.org/10.1109/ICCV.2015.169

    Article  Google Scholar 

  • Guo C, Zhang Y, Jiang L, Meng W, Yuben DU, Chuntian MA (2017) Discussion on the environmental and engineering geological problems along the Sichuan-Tibet railway and its adjacent area. Geoscience 31(5):4–16 (In Chinese)

    Google Scholar 

  • Guo C, Wu R, Jiang W, Zhong N, Wang Y, Wang D, Zhang Y, Yang Z, Meng W, Li X, Liu G (2021) Typical geohazards and engineering geological problems along the Ya’an-Linzhi section of the Sichuan-Tibet railway, China. Geoscience 35(01):1–17

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1):181–216

    Article  Google Scholar 

  • Guzzetti F, Mondini A, Cardinali M, Fiorucci F, Santangelo M, Chang K (2012) Landslide inventory maps: new tools for an old problem. Earth-Science Rev 112:42–66

    Article  Google Scholar 

  • Hao X, Zhang G, Ma S (2016) Deep learning. Int J Semant Comput 10(03):417–439

    Article  Google Scholar 

  • He S, Peng P, Lan D, 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

    Article  Google Scholar 

  • Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Bui DT (2017) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int. https://doi.org/10.1080/10106049.2015.1130086

    Article  Google Scholar 

  • Hong H, Miao Y, Liu J, Zhu A (2019) Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. CATENA 176:45–64

    Article  Google Scholar 

  • Hong H, Liu J, Zhu A (2020) Modeling landslide susceptibility using logitboost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718(18):137231

    Article  Google Scholar 

  • Huang R (2007) Large-scale landslides and their sliding mechanisms in china since the 20th century. Chin J Rock Mech Eng 26(3):433–454 (In Chinese)

    Google Scholar 

  • Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529

    Article  Google Scholar 

  • Huang R, Yan R, Qu K, Wang K (2013) Engineering geological assessment for route selection of railway line in geologically active area: a case study in China. J Mt Sci 10(4):495–508

    Article  Google Scholar 

  • 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 191:104580

    Article  Google Scholar 

  • Hussain MA, Chen Z, Kalsoom I et al (2022) Landslide susceptibility mapping using machine learning algorithm: a case study along Karakoram highway (KKH), Pakistan. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-021-01451-1

    Article  Google Scholar 

  • Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility Map. Landslides 13(2):379–397

    Article  Google Scholar 

  • Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Huebl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik district, Uzbekistan. Sci Total Environ 653:801–814

    Article  Google Scholar 

  • Kadavi PR, Lee CW, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10(8):1252. https://doi.org/10.3390/rs10081252

    Article  Google Scholar 

  • Kjekstad O, Highland L (2009) Economic and social impacts of landslides. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-69970-5_30

    Book  Google Scholar 

  • Korzeniowska K, Pfeifer N, Landtwing S (2018) Mapping gullies, dunes, lava fields, and landslides via surface roughness. Geomorphology. https://doi.org/10.1016/j.geomorph.2017.10.011

    Article  Google Scholar 

  • Lan H, Zhang N, Li L, Tian N, Zhang Y, Liu S, Lin G, Tian C, Wu Y, Yao J, Peng J, Zhou C (2021) Risk analysis of major engineering geological hazards for Sichuan—Tibet railway in the phase of feasibility study. J Eng Geol 29(02):326–341 (In Chinese)

    Google Scholar 

  • Lucchese LV, Oliveira G, Pedrollo OC (2021) Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using artificial neural networks. Catena 198:105067

    Article  Google Scholar 

  • Mahdi P, Abolfazl J, Ataollah S, Himan S, Omid R, Ebrahim O, Dieu TB (2021) Deep learning neural networks for spatially explicit prediction of flash flood probability. Geosci Front 03:376–389

    Google Scholar 

  • Mandal K, Saha S, Mandal S (2021) Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geosci Front. https://doi.org/10.1016/j.gsf.2021.101203

    Article  Google Scholar 

  • Martín B, Alonso JC, Martín CA, Palacín C, Magaña M, Alonso J (2012) Influence of spatial heterogeneity and temporal variability in habitat selection: a case study on a great bustard metapopulation. Ecol Model 228:39–48

    Article  Google Scholar 

  • Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Sci Soc Am J. https://doi.org/10.2136/sssaj1986.03615995005000050042x

    Article  Google Scholar 

  • Mousa A, Bahareh G, Ataollah S, Himan S, Kamran C, Thai PB, Baharin BA, Dieu TB (2018) A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int. https://doi.org/10.1080/10106049.2018.1499820

    Article  Google Scholar 

  • Ngo P, Panahi M, Khosravi K, Ghorbanzadeh O et al (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12(2):15

    Google Scholar 

  • Nguyen BQV, Kim YT (2021) Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches. Bull Eng Geol Environ 80:4291–4321

    Article  Google Scholar 

  • Nsengiyumva JB, Luo G, Hakorimana E, Mind’Je R, Gasirabo A, Mukanyandwi V (2019) Comparative analysis of deterministic and semiquantitative approaches for shallow landslide risk modeling in Rwanda. Risk Anal. https://doi.org/10.1111/risa.13359

    Article  Google Scholar 

  • Ouyang C, An H, Zhou S, Wang Z, Su P, Wang D, Cheng D, She J (2019) Insights from the failure and dynamic characteristics of two sequential landslides at Baige village along the Jinsha River, China. Landslides 16:1397–1414

    Article  Google Scholar 

  • Peng J, Cui P, Zhuang J (2020) Challenges to engineering geology of Sichuan—Tibet railway. Chin J Rock Mech Eng 12:2377–2389 (In Chinese)

    Google Scholar 

  • Pham BT, Bui DT, Indra P, Dholakia MB (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS—based statistical approach of frequency ratio method. Int J Eng Techn Res V4(11):338–344

    Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia MB (2016) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards 83:97–127

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I (2017) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and j48 decision trees methods: a comparative study. Geotech Geol Eng 35:2597–2611

    Article  Google Scholar 

  • Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Prakash I (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195:104805

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: safarood basin, Iran. Catena 97(15):71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • 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(6):747–759

    Article  Google Scholar 

  • Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 18:60–91

    Article  Google Scholar 

  • Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566:195–204

    Article  Google Scholar 

  • Rosi A, Tofani V, Tanteri L, Tacconi SC, Agostini A, Catani F, Casali N (2018) The new landslide inventory of Tuscany (Italy) updated with PSInSAR: geomorphological features and landslide distribution. Landslides 15:5–19

    Article  Google Scholar 

  • Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63:49–63

    Article  Google Scholar 

  • Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA. https://doi.org/10.1016/j.catena.2019.104249

    Article  Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  • Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  Google Scholar 

  • Skilodimou HD, Bathrellos GD, Chousianitis K, Youssef AM, Pradhan Β (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78(2):47

    Article  Google Scholar 

  • Sun D, Xu J, Wen H, Wang D (2020) Assessment of landslide susceptibility mapping based on bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol 281:105972

    Article  Google Scholar 

  • Sun D, Shi S, Wen H, Xu J, Zhou X, Wu J (2021) A hybrid optimization method of factor screening predicated on geodetector and random forest for landslide susceptibility mapping. Geomorphology 379(1):107623

    Article  Google Scholar 

  • Tolga G (2019) Tectonic, topographic and rock-type influences on large landslides at the northern margin of the Anatolian Plateau. Landslides 16:333–346

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan county, China. Sci Total Environ 666:975–993

    Article  Google Scholar 

  • Wang Y, Fang Z, Wang M, Ling P, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445

    Article  Google Scholar 

  • Wang Y, Feng L, Li S, Ren F, Du Q (2020) A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in zhejiang province, china. Catena 188:104425

    Article  Google Scholar 

  • Wang S, Zhuang J, Zheng J, Fan H, Kong J, Zhan J (2021) Application of Bayesian hyperparameter optimized random forest and XGBoost model for landslide susceptibility mapping. Front Earth Sci 9:712240

    Article  Google Scholar 

  • Wu Y, Ke Y, Chen Z, Liang S, Hong H (2020) Application of alternating decision tree with adaboost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396

    Article  Google Scholar 

  • Xiang L, Cui P, Zhang J, Huang D, Zhou X (2010) Triggering factors susceptibility of earthquake-induced collapses and landslides in wenchuan county. J Sichuan Univ 42(5):105–112 (Engineering Science Edition)

    Google Scholar 

  • Yao X, Tham L, Dai F (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582

    Article  Google Scholar 

  • Yao J, Qin S, Qiao S, Che W, Chen Y, Su G, Miao Q (2020) Assessment of landslide susceptibility combining deep learning with semi-supervised learning in Jiaohe county, Jilin province, China. Appl Sci 10(16):5640. https://doi.org/10.3390/app10165640

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from kat landslides (tokat—turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  • Yin J, Chen J, Xu X, Wang X, Zheng Y (2010) The characteristics of the landslides triggered by the wenchuan m-s 8.0 earthquake from anxian to beichuan. J Asian Earth Sci 37(5–6):452–459

    Article  Google Scholar 

  • Yu L, Cao Y, Zhou C, Wang Y, Huo Z (2019) Landslide susceptibility mapping combining information gain ratio and support vector machines: a case study from wushan segment in the three gorges reservoir area, China. Appl Sci 9(22):4756

    Article  Google Scholar 

  • Zezere JL, Ferreira ADB, Rodrigues ML (1999) The role of conditioning and triggering factors in the occurrence of landslides: a case study in the area north of lisbon (portugal). Geomorphology 30(30):133–146

    Article  Google Scholar 

  • Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the three gorges reservoir area, China. Environ Earth Sci 76(10):405

    Article  Google Scholar 

  • Zhang L, Xiao T, He J et al (2019) Erosion-based analysis of breaching of Baige landslide dams on the Jinsha River, China, in 2018. Landslides 16:1965–1979

    Article  Google Scholar 

  • Zhang Q, Ling S, Li X, Sun C, Xu J, Huang T (2020) Comparison of landslide susceptibility mapping rapid assessment models in Jiuzhaigou County, Sichuan province, China. Chin J Rock Mech Eng 39(08):1595–1610 (In Chinese)

    Google Scholar 

  • Zhang C, Li Z, Yu C et al (2021) Landslide detection: application of InSAR stacking in the Jinsha River basin with the aid of GACOS. Geomat Inform Sci Wuhan Univ. https://doi.org/10.13203/j.whugis20200675

    Article  Google Scholar 

  • Zhao Z, Chen W, Pourghasemi HR (2017) A gis-based comparative study of dempster-shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int. https://doi.org/10.1080/10106049.2016.1140824

    Article  Google Scholar 

  • Zhao F, Meng X, Zhang Y, Chen G, Yue D (2019) Landslide susceptibility mapping of karakorum highway combined with the application of sbas-insar technology. Sensors 19(12):2685

    Article  Google Scholar 

  • Zhou J, Cui P, Hao M (2016) Comprehensive analyses of the initiation and entrainment processes of the 2000 Yigong catastrophic landslide in Tibet, China. Landslides 13(1):39–54

    Article  Google Scholar 

  • Zhuang J, Peng J (2014) A coupled slope cutting—a prolonged rainfall-induced loess landslide: a 17 October 2011 case study. Bull Eng Geol Env 73(4):997–1011

    Article  Google Scholar 

  • Zhuang J, Peng J, Wang G, Lqbal J, Zhu X (2016) Prediction of rainfall-induced shallow landslides in the loess plateau, yan’an, China, using the trigrs model. Earth Surf Process Landforms. https://doi.org/10.1002/esp.4050

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Prof. Wu Zhu for providing some InSAR data to support this research.

Funding

This study was financially supported by the National Natural Science Foundation of China (No. 41941019 and 41922054), National Key Research and Development Program of China (No. 2020YFC1512000).

Author information

Authors and Affiliations

  1. College of Geological Engineering and Geomatics/Key Laboratory of Western China Mineral Resources and Geological Engineering, Chang’an University, Xi’an, 710054, Shaanxi, China

    Shibao Wang, Jianqi Zhuang, Jiaqi Mu, Jia Zheng, Jiewei Zhan, Jie Wang & Yuting Fu

Authors
  1. Shibao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianqi Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaqi Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiewei Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuting Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianqi Zhuang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Zhuang, J., Mu, J. et al. Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning. Environ Earth Sci 81, 250 (2022). https://doi.org/10.1007/s12665-022-10375-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-022-10375-z

Keywords

Search

Navigation