Abstract
Accurate evaluation of landslide susceptibility is very important to ensure the safe operation of mountain highways. The Sichuan-Tibet Highway, which traverses the east of the Tibetan Plateau, frequently encounters natural hazards. Previous studies generally use statistical methods to analyze the hazards along the Sichuan-Tibet Highway. In this research, we present two road factors, namely aspect to road and road profile to increase the accuracy of landslide susceptibility mapping by considering the influence of landslide movement direction on road. First, the aspect to road, which represents the impact of different landslide movement directions on the highway, was extracted by combining road direction with mountain aspect. Then, the road profile, which reflects the subgrade structure between the road and surrounding mountains, was extracted according to the terrain data. Finally, the landslide susceptibility maps were produced based on the random forest (RF) method by using 473 landslides and 10 conditioning factors, including road factors (aspect to road, road profile) and primitive factors (slope, aspect, curvature, relief amplitude, peak ground acceleration, crustal movement velocity, faults, rainfall). The area under the receiver operating characteristic curve (AUC) and the Gini importance were used to evaluate the performance of proposed road factors. The AUC values on two groups that add road factors and only use primitive factors were 0.8517 and 0.8243, respectively. The Gini importance indicated that road profile (0.123) and aspect to road (0.116) have a significant contribution to landslides compared with the primitive factors. The results of multi-collinearity analysis and frequency ratio confirmed the suitability of the road factors for predicting hazards along the highway.
Similar content being viewed by others
References
Achour Y, Boumezbeur A, Hadji R, et al (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10:194. https://doi.org/10.1007/s12517-017-2980-6
Achour Y, Garçia S, Cavaleiro V (2018) GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area, Northwest Covilhã, Portugal. Arab J Geosci 2018 1118 11:1–17. https://doi.org/10.1007/S12517-018-3920-9
Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?. Geosci Front 11:871–883. https://doi.org/10.1016/J.GSF.2019.10.001
Achour Y, Saidani Z, Touati R, et al (2021) Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environ Earth Sci 80:575. https://doi.org/10.1007/s12665-021-09889-9
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
Arabameri A, Pradhan B, Rezaei K, et al (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci 16:595–618. https://doi.org/10.1007/s11629-018-5168-y
Arabameri A, Saha S, Roy J, et al (2020) Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River watershed, Iran. Remote Sens 12(12):475. https://doi.org/10.3390/rs12030475
Chang Z, Du Z, Zhang F, et al (2020) Landslide susceptibility prediction based on remote sensing images and gis: comparisons of supervised and unsupervised machine learning models. Remote Sens 12(3):502. https://doi.org/10.3390/rs12030502
Chen HX, Zhang LM (2014) A physically-based distributed cell model for predicting regional rainfall-induced shallow slope failures. Eng Geol 176:79–92. https://doi.org/10.1016/j.enggeo.2014.04.011
Chen W, Pourghasemi HR, Panahi M, et al (2017a) Spatial prediction of landslide susceptibility using an adaptive neurofuzzy 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
Chen W, Shahabi H, Zhang S, et al (2018) Landslide susceptibility modeling based on GIS and Novel Bagging-based Kernel logistic regression. Appl Sci 8:2540. https://doi.org/10.3390/app8122540
Chen W, Xie X, Wang J, et al (2017b) 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 Z, He C, Yang W, et al (2020) Impacts of geological conditions on instability causes and mechanical behavior of large-scale tunnels: a case study from the Sichuan-Tibet highway, China. Bull Eng Geol Environ 79:3667–3688. https://doi.org/10.1007/s10064-020-01796-w
Choi CE, Cui Y, Au KYK, et al (2018) Case Study: Effects of a partial-debris dam on riverbank erosion in the Parlung Tsangpo River, China. Water (Switzerland) 10:250. https://doi.org/10.3390/w10030250
Delaney KB, Evans SG (2015) The 2000 Yigong landslide (Tibetan Plateau), rockslide-dammed lake and outburst flood: Review, remote sensing analysis, and process modelling. Geomorphology 246:377–393. https://doi.org/10.1016/j.geomorph.2015.06.020
Di Napoli M, Marsiglia P, Di Martire D, et al (2020) Landslide susceptibility assessment of wildfire burnt areas through earth-observation techniques and a machine learning-based approach. Remote Sens 12(15):2505. https://doi.org/10.3390/rs12152505
Du G, Zhang Y, Yang Z, et al (2019) Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods. Bull Eng Geol Environ 78:4201–4215. https://doi.org/10.1007/s10064-018-1393-4
Fallah-Zazuli M, Vafaeinejad A, Alesheykh AA, et al (2019) Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models. Earth Sci Informatics 12:615–628. https://doi.org/10.1007/s12145-019-00389-w
Fan L, Lehmann P, McArdell B, Or D (2017) Linking rainfall-induced landslides with debris flows runout patterns towards catchment scale hazard assessment. Geomorphology 280:1–15. https://doi.org/10.1016/j.geomorph.2016.10.007
Fan X, Yang F, Siva Subramanian S, et al (2020) Prediction of a multi-hazard chain by an integrated numerical simulation approach: the Baige landslide, Jinsha River, China. Landslides 17:147–164. https://doi.org/10.1007/s10346-019-01313-5
Ge Y, Cui P, Su F, et al (2014) Case history of the disastrous debris flows of Tianmo Watershed in Bomi County, Tibet, China: Some mitigation suggestions. J Mt Sci 11:1253–1265. https://doi.org/10.1007/s11629-014-2579-2
Ghorbanzadeh O, Blaschke T, Gholamnia K, et al (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 11(2):21. https://doi.org/10.3390/rs11020196
Guo C, Montgomery DR, Zhang Y, et al (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110. https://doi.org/10.1016/j.geomorph.2015.07.012
Guo Y, Ge Y, Cui P, et al (2021) Early and mid-Holocene hydroclimate change recorded in tufa deposits in the Jiuzhaigou gully, eastern Tibetan Plateau. Catena 196:104834. https://doi.org/10.1016/j.catena.2020.104834
Han Y, Dong S, Chen Z, et al (2014) Assessment of secondary mountain hazards along a section of the Dujiangyan-Wenchuan highway. J Mt Sci 11:51–65. https://doi.org/10.1007/s11629-012-2516-1
He Q, Shahabi H, Shirzadi A, et al (2019) Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Env 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Huang F, Yin K, Huang J, et al (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
Huang F, Zhang J, Zhou C, et al (2019) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17:217–229. https://doi.org/10.1007/s10346-019-01274-9
Hungr O, Leroueil S, Picarelli L (2013) The Varnes classification of landslide types, an update. Landslides 2013 112 11:167–194. https://doi.org/10.1007/S10346-013-0436-Y
Jaafari A, Najafi A, Pourghasemi HR, et al (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
Jaafari A, Panahi M, Pham BT, et al (2019) Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena 175:430–445. https://doi.org/10.1016/j.catena.2018.12.033
Kalantar B, Ueda N, Saeidi V, et al (2020) Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens 12(11):1737. https://doi.org/10.3390/rs12111737
Kausar N, Majid A (2016) Random forest-based scheme using feature and decision levels information for multi-focus image fusion. Pattern Anal Appl 19:221–236. https://doi.org/10.1007/s10044-015-0448-4
Li Y, Cui P, Ye C, et al (2021) Accurate prediction of earthquake-induced landslides based on deep learning considering landslide source area. Remote Sens 13:3436. https://doi.org/10.3390/rs13173436
Long N, Smedt F (2018) Analysis and Mapping of Rainfall-Induced Landslide Susceptibility in A Luoi District, Thua Thien Hue Province, Vietnam. Water 11:51. https://doi.org/10.3390/w11010051
Ma S, Xu C, Shao X (2020) Spatial prediction strategy for landslides triggered by large earthquakes oriented to emergency response, mid-term resettlement and later reconstruction. Int J Disaster Risk Reduct 43:101362. https://doi.org/10.1016/j.ijdrr.2019.101362
Merghadi A, Abderrahmane B, Tien Bui D (2018) Landslide susceptibility assessment at Mila basin (Algeria): A comparative assessment of prediction capability of advanced machine learning methods. ISPRS Int J Geo-Information 7:268. https://doi.org/10.3390/ijgi7070268
Merghadi A Yunus AP, Dou J, et al (2020) Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Rev 207: 103225. https://doi.org/10.1016/j.earscirev.2020.103225
Nhu V-H, Hoang ND, Nguyen H, et al (2020) Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena 188(7). https://doi.org/10.1016/j.cxatena.2020.104458
Ouimet WB, Whipple KX, Royden LH, et al (2007) The influence of large landslides on river incision in a transient landscape: Eastern margin of the Tibetan Plateau (Sichuan, China). GSA Bull 119:1462–1476. https://doi.org/10.1130/B26136.1%JGSABulletin
Pham BT, Tien Bui D, Dholakia MB, et al (2016) A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area. Geotech Geol Eng 34:1807–1824. https://doi.org/10.1007/s10706-016-9990-0
Pirasteh S, Shamsipour G, Liu G, et al (2020) A new algorithm for landslide geometric and deformation analysis supported by digital elevation models. Earth Sci Informatics 13:361–375. https://doi.org/10.1007/s12145-019-00437-5
Pourghasemi HR, Kornejady A, Kerle N, Shabani F (2020) Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena 187:104364. https://doi.org/10.1016/j.catena.2019.104364
Pourghasemi HR, Rossi M (2017) 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 130:609–633. https://doi.org/10.1007/s00704-016-1919-2
Reichenbach P, Rossi M, Malamud BD, et al (2018) A review of statistically-based landslide susceptibility models. Earth-Science Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena 186(6):104249. https://doi.org/10.1016/j.catena.2019.104249
Sar N, Khan A, Chatterjee S, et al (2016) Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin, India. Int Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2016.09.004
Shang Y, Park HD, Yang Z, Yang J (2005) Distribution of landslides adjacent to the northern side of the Yarlu Tsangpo Grand Canyon in Tibet, China. Environ Geol 48:721–741. https://doi.org/10.1007/s00254-005-0012-4
Shang Y, Yang Z, Li L, et al (2003) A super-large landslide in Tibet in 2000: background, occurrence, disaster, and origin. Geomorphology 54:225–243. https://doi.org/10.1016/S0169-555X(02)00358-6
Shirzadi A, Solaimani K, Roshan MH, et al (2019) Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. Catena 178:172–188. https://doi.org/10.1016/j.catena.2019.03.017
Shrestha S, Kang T-S, Choi JC (2018) Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal. J Earth Syst Sci 127:38. https://doi.org/10.1007/s12040-018-0936-1
Song Y, Gong J, Gao S, et al (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: A case study in Beichuan, China. Comput Geosci 42:189–199. https://doi.org/10.1016/j.cageo.2011.09.011
Wang E, Burchfiel BC (2000) Late Cenozoic to Holocene deformation in southwestern Sichuan and adjacent Yunnan, China, and its role in formation of the southeastern part of the Tibetan Plateau. GSA Bull 112:413–423. https://doi.org/10.1130/0016-7606(2000)112<413:LCTHDI>2.0.CO;2%JGSABulletin
Wang F, Xu P, Wang C, et al (2017) Application of a gis-based slope unit method for landslide susceptibility mapping along the longzi river, southeastern tibetan plateau, China. ISPRS Int J Geo-Information 6:172. https://doi.org/10.3390/ijgi6060172
Wang W, He Z, Han Z, et al (2020) Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China. Nat Hazards 103:3239–3261. https://doi.org/10.1007/s11069-020-04128-z
Wu C, Guo Y, Su L (2021) Risk assessment of geological disasters in Nyingchi, Tibet. Open Geosci 13:219–232. https://doi.org/10.1515/GEO-2020-0208
Xu R, Lin H, Lü Y, et al (2018) A modified change vector approach for quantifying land cover change. Remote Sens 10(10):1578. https://doi.org/10.3390/rs10101578
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
Ye C, Cui P, Pirasteh S, et al (2016) GiT-based structural geologic feature analysis of the southern segment of Longmenshan fault zone for earthquake evidence. J Mt Sci 13:906–916. https://doi.org/10.1007/s11629-015-3796-z
Ye C, Li Y, Cui P, et al (2019) Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains. IEEE J Sel Top Appl Earth Obs Remote Sens 12:5047–5060. https://doi.org/10.1109/jstars.2019.2951725
Yin G, Luo J, Niu F, et al (2021) Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau. Landslides 18(10):2639–2649. https://doi.org/10.1007/s10346-021-01669-7
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) 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. Landslides 13:839–856. https://doi.org/10.1007/s10346-015-0614-1
Zhao B, Li W, Wang Y, et al (2019) Landslides triggered by the Ms 6.9 Nyingchi earthquake, China (18 November 2017): analysis of the spatial distribution and occurrence factors. Landslides 16:765–776. https://doi.org/10.1007/s10346-019-01146-2
Zhao Y, Xu M, Guo J, et al (2014) Accumulation characteristics, mechanism, and identification of an ancient translational landslide in China. Landslides 2014 126 12:1119–1130. https://doi.org/10.1007/S10346-014-0535-4
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 13:39–54. https://doi.org/10.1007/s10346-014-0553-2
Acknowledgements
This work was supported in part by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) under Grant 2019QZKK0902, the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA23090203, and the National Natural Science Foundation of China under Grant 42071411.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ye, Cm., Wei, Rl., Ge, Yg. et al. GIS-based spatial prediction of landslide using road factors and random forest for Sichuan-Tibet Highway. J. Mt. Sci. 19, 461–476 (2022). https://doi.org/10.1007/s11629-021-6848-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-021-6848-6