Landslide Susceptibility Mapping using Machine Learning Algorithm

Muhammad Afaq Hussain, Zhanlong Chen, Run Wang, Safeer Ullah Shah, Muhammad Shoaib, Nafees Ali, Daozhu Xu, Chao Ma


Landslides are natural disasters that have resulted in the loss of economies and lives over the years. The landslides caused by the 2005 Muzaffarabad earthquake heavily impacted the area, and slopes in the region have become unstable. This research was carried out to find out which areas, as in Muzaffarabad district, are sensitive to landslides and to define the relationship between landslides and geo-environmental factors using three tree-based classifiers, namely, Extreme Gradient Boosting (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). These machine learning models are innovative and can assess environmental problems and hazards for any given area on a regional scale. The research consists of three steps: Firstly, for training and validation, 94 historical landslides were randomly split into a proportion of 7/3. Secondly, topographical and geological data as well as satellite imagery were gathered, analyzed, and built into a spatial database using GIS Environment. Nine layers of landslide-conditioning factors were developed, including Aspect, Elevation, Slope, NDVI, Curvature, SPI, TWI, Lithology, and Landcover. Finally, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) value were used to estimate the model's efficiency. The area under the curve values for the RF, XGBoost, and KNN models are 0.895 (89.5%), 0.893 (89.3%), and 0.790 (79.0%), respectively. Based on the three machine learning techniques, the innovative outputs show that the performance of the Random Forest model has a maximum AUC value of 0.895, and it is more efficient than the other tree-based classifiers. Elevation and Slope were determined as the most important factors affecting landslides in this research area. The landslide susceptibility maps were classified into four classes: low, moderate, high, and very high susceptibility. The result maps are useful for future generalized construction operations, such as selecting and conserving new urban and infrastructural areas.


Doi: 10.28991/CEJ-2022-08-02-02

Full Text: PDF


Landslide Susceptibility Modelling; Proportion; Efficient; Random Forest.


Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers and Geosciences, 37(9), 1264–1276. doi:10.1016/j.cageo.2010.10.012.

Faraji Sabokbar, H., Shadman Roodposhti, M., & Tazik, E. (2014). Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology, 226, 15–24. doi:10.1016/j.geomorph.2014.07.026.

Taalab, K., Cheng, T., & Zhang, Y. (2018). Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159–178. doi:10.1080/20964471.2018.1472392.

Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific Reports, 5, 1–15. doi:10.1038/srep09899.

Bui, D. T., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B. Bin, Panahi, M., Hong, H., & Tian, Y. (2018). Landslide detection and susceptibility mapping by AIRSAR data using support vector machine and index of entropy models in Cameron Highlands, Malaysia. Remote Sensing, 10(10), 1527. doi:10.3390/rs10101527.

Dou, J., Yunus, A. P., Xu, Y., Zhu, Z., Chen, C. W., Sahana, M., Khosravi, K., Yang, Y., & Pham, B. T. (2019). Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China. Natural Hazards, 97(2), 579–609. doi:10.1007/s11069-019-03659-4.

Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3–4), 99–111. doi:10.1016/j.enggeo.2008.03.014.

Basharat, M., Rohn, J., Ehret, D., & Baig, M. S. (2012). Lithological and structural control of Hattian Bala rock avalanche triggered by the Kashmir earthquake 2005, sub-Himalayas, northern Pakistan. Journal of Earth Science, 23(2), 213–224. doi:10.1007/s12583-012-0248-3.

Dunning, S. A., Mitchell, W. A., Rosser, N. J., & Petley, D. N. (2007). The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir Earthquake of 8 October 2005. Engineering Geology, 93(3–4), 130–144. doi:10.1016/j.enggeo.2007.07.003.

Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1–19. doi:10.1007/s12517-016-2308-y.

Basharat, M., & Rohn, J. (2015). Effects of volume on travel distance of mass movements triggered by the 2005 Kashmir earthquake, in the Northeast Himalayas of Pakistan. Natural Hazards, 77(1), 273–292. doi:10.1007/s11069-015-1590-4.

Baeten, N. J., Laberg, J. S., Forwick, M., Vorren, T. O., Vanneste, M., Forsberg, C. F., Kvalstad, T. J., & Ivanov, M. (2013). Morphology and origin of smaller-scale mass movements on the continental slope off northern Norway. Geomorphology, 187, 122–134. doi:10.1016/j.geomorph.2013.01.008.

Shafique, M., van der Meijde, M., & Khan, M. A. (2016). A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective. Journal of Asian Earth Sciences, 118, 68–80. doi:10.1016/j.jseaes.2016.01.002.

Komac, M. (2006). A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia. Geomorphology, 74(1–4), 17–28. doi:10.1016/j.geomorph.2005.07.005.

Wachal, D. J., & Hudak, P. F. (2000). Mapping landslide susceptibility in Travis County, Texas, USA. GeoJournal, 51(3), 245–253. doi:10.1023/A:1017524604463.

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). Computers and Geosciences, 35(6), 1125–1138. doi:10.1016/j.cageo.2008.08.007.

Zhao, X., & Chen, W. (2020). GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Applied Sciences (Switzerland), 10(1), 16. doi:10.3390/app10010016.

Jaafari, A., Najafi, A., Pourghasemi, H. R., Rezaeian, J., & Sattarian, A. (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4), 909–926. doi:10.1007/s13762-013-0464-0.

Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2002). A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48(4), 349–364. doi:10.1016/S0169-555X(02)00079-X.

Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44(7), 820–833. doi:10.1007/s00254-003-0825-y.

Yao, X., Tham, L. G., & Dai, F. C. (2008). Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572–582. doi:10.1016/j.geomorph.2008.02.011.

Jaafari, A., Rezaeian, J., & Omrani, M. S. (2017). Spatial prediction of slope failures in support of forestry operations safety. Croatian Journal of Forest Engineering, 38(1), 107–118.

Wu, Y., Ke, Y., Chen, Z., Liang, S., Zhao, H., & Hong, H. (2020). Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena, 187, 104396. doi:10.1016/j.catena.2019.104396.

Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). 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. doi:10.1016/j.catena.2016.11.032.

Wang, G., Lei, X., Chen, W., Shahabi, H., & Shirzadi, A. (2020). Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry, 12(3), 325. doi:10.3390/sym12030325.

Rahmati, O., Falah, F., Naghibi, S. A., Biggs, T., Soltani, M., Deo, R. C., Cerdà, A., Mohammadi, F., & Tien Bui, D. (2019). Land subsidence modelling using tree-based machine learning algorithms. Science of the Total Environment, 672, 239–252. doi:10.1016/j.scitotenv.2019.03.496.

Dao, D. Van, Jaafari, A., Bayat, M., Mafi-Gholami, D., Qi, C., Moayedi, H., Phong, T. Van, Ly, H. B., Le, T. T., Trinh, P. T., Luu, C., Quoc, N. K., Thanh, B. N., & Pham, B. T. (2020). A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena, 188, 104451. doi:10.1016/j.catena.2019.104451.

Kamp, U., Growley, B. J., Khattak, G. A., & Owen, L. A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology, 101(4), 631–642. doi:10.1016/j.geomorph.2008.03.003.

Riaz, M. T., Basharat, M., Hameed, N., Shafique, M., & Luo, J. (2018). A Data-Driven Approach to Landslide-Susceptibility Mapping in Mountainous Terrain: Case Study from the Northwest Himalayas, Pakistan. Natural Hazards Review, 19(4), 05018007. doi:10.1061/(asce)nh.1527-6996.0000302.

Juez, C., Murillo, J., & García-Navarro, P. (2013). 2D simulation of granular flow over irregular steep slopes using global and local coordinates. Journal of Computational Physics, 255, 166–204. doi:10.1016/

Iverson, R. M., & George, D. L. (2016). Modelling landslide liquefaction, mobility bifurcation and the dynamics of the 2014 Oso disaster. Geotechnique, 66(3), 175–187. doi:10.1680/jgeot.15.LM.004.

Lacasta, A., Juez, C., Murillo, J., & García-Navarro, P. (2015). An efficient solution for hazardous geophysical flows simulation using GPUs. Computers and Geosciences, 78, 63–72. doi:10.1016/j.cageo.2015.02.010.

Li, R., & Wang, N. (2019). Landslide susceptibility mapping for the Muchuan County (China): A comparison between bivariate statistical models (WoE, EBF, and IoE) and their ensembles with logistic regression. Symmetry, 11(6), 762. doi:10.3390/sym11060762.

Wang, Q., Li, W., Chen, W., & Bai, H. (2015). GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang county of Baoji city, China. Journal of Earth System Science, 124(7), 1399–1415. doi:10.1007/s12040-015-0624-3.

Dietterich, T. G. (2000). Ensemble methods in machine learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1857 LNCS, 1–15. doi:10.1007/3-540-45014-9_1.

Graczyk, M., Lasota, T., Trawiński, B., & Trawiński, K. (2010). Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5991 LNAI(PART 2), 340–350. doi:10.1007/978-3-642-12101-2_35.

Pham, B. T., Tien Bui, D., & Prakash, I. (2017). Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study. Geotechnical and Geological Engineering, 35(6), 2597–2611. doi:10.1007/s10706-017-0264-2.

Pham, B. T., Phong, T. Van, Nguyen-Thoi, T., Parial, K., K. Singh, S., Ly, H. B., Nguyen, K. T., Ho, L. S., Le, H. Van, & Prakash, I. (2020). Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto International, 1–23. doi:10.1080/10106049.2020.1737972.

Khan, U., & Rehman, D. H. (Eds.). (2017). Landslide Hazard, Vulnerability and Risk Analysis of Muzaffarabad City. doi:10.4172/978-1-63278-001-0-002.

Chen, J., Yang, S., Li, H., Zhang, B., & Lv, J. (2013). Research on geographical environment unit division based on the method of natural breaks (Jenks). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(4W3), 47–50. doi:10.5194/isprsarchives-XL-4-W3-47-2013.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. doi:10.1007/bf00058655.

Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R. F., & Petkov, N. (2018). Predicting Slaughter Weight in Pigs with Regression Tree Ensembles. In Proceedings of APPIS; pp. 1-9, doi:10.3233/978-1-61499-949-4-1.

Sahin, E. K. (2020). Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto International, 1–25. doi:10.1080/10106049.2020.1831623.

Marjanović, M., Bajat, B., & Kovačević, M. (2009). Landslide susceptibility assessment with machine learning algorithms. International Conference on Intelligent Networking and Collaborative Systems, INCoS 2009, 273–278. doi:10.1109/INCOS.2009.25.

Miner, A., P. Vamplew, D. J. Windle, Flentje, P., & P. Warner. (2010). A Comparative Study of Various Data Mining Techniques as applied to the Modeling of Landslide Susceptibility on the Bellarine Peninsula. In Faculty of Engineering - Papers (pp. 1327–1336).

Bröcker, J., & Smith, L. A. (2007). Increasing the reliability of reliability diagrams. Weather and Forecasting, 22(3), 651–661. doi:10.1175/WAF993.1.

Chen, J. S., Huang, H. Y., & Hsu, C. Y. (2020). A kNN Based Position Prediction Method for SNS Places. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12034 LNAI, 266–273. doi:10.1007/978-3-030-42058-1_22.

Kala, R. (2012). Multi-robot path planning using co-evolutionary genetic programming. Expert Systems with Applications, 39(3), 3817–3831. doi:10.1016/j.eswa.2011.09.090.

Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., Zhu, A. X., Chen, W., & Ahmad, B. Bin. (2018). Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399–413. doi:10.1016/j.catena.2018.01.005.

Tien Bui, D., Tuan, T. A., 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(2), 361–378. doi:10.1007/s10346-015-0557-6.

Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., & Chacón, J. (2012). Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain). Natural Hazards and Earth System Science, 12(2), 327–340. doi:10.5194/nhess-12-327-2012.

Süzen, M. L., & Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate. Environmental Geology, 45(5), 665–679. doi:10.1007/s00254-003-0917-8.

Xiong, K., Adhikari, B. R., Stamatopoulos, C. A., Zhan, Y., Wu, S., Dong, Z., & Di, B. (2020). Comparison of different machine learning methods for debris flow susceptibility mapping: A case study in the Sichuan Province, China. Remote Sensing, 12(2), 295. doi:10.3390/rs12020295.

Dou, J., Yunus, A. P., Tien Bui, D., Merghadi, A., Sahana, M., Zhu, Z., Chen, C. W., Khosravi, K., Yang, Y., & Pham, B. T. (2019). Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment, 662, 332–346. doi:10.1016/j.scitotenv.2019.01.221.

Torizin, J., Fuchs, M., Awan, A. A., Ahmad, I., Akhtar, S. S., Sadiq, S., Razzak, A., Weggenmann, D., Fawad, F., Khalid, N., Sabir, F., & Khan, A. J. (2017). Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan. Natural Hazards, 89(2), 757–784. doi:10.1007/s11069-017-2992-2.

Dou, J., Yunus, A. P., Bui, D. T., Sahana, M., Chen, C. W., Zhu, Z., Wang, W., & Pham, B. T. (2019). Evaluating gis-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the lidar dem. Remote Sensing, 11(6), 638. doi:10.3390/rs11060638.

Thai Pham, B., Tien Bui, D., & Prakash, I. (2018). Landslide susceptibility modelling using different advanced decision trees methods. Civil Engineering and Environmental Systems, 35(1–4), 139–157. doi:10.1080/10286608.2019.1568418.

Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. doi:10.1016/j.earscirev.2020.103225.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. doi:10.1145/2939672.2939785.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. doi:10.1016/j.patrec.2005.10.010.

Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, NJ, United States.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324.

Full Text: PDF

DOI: 10.28991/CEJ-2022-08-02-02


  • There are currently no refbacks.

Copyright (c) 2022 Muhammad Afaq Hussain, Zhanlong chen, Run Wang, Safeer Ullah Shah, Muhammad Shoaib, Nafees Ali

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.