Abstract
Along mountain roads, rainfall-triggered landslides are typical disasters that cause significant human casualties. Thus, to establish effective mitigation measures, it would be very useful were government agencies and practicing land-use planners to have the capability to make an accurate landslide evaluation. Here, we propose a machine learning methodology for the spatial prediction of rainfall-induced landslides along mountain roads which is based on a random forest classifier (RFC) and a GIS-based dataset. The RFC is used as a supervised learning technique to generalize the classification boundary that separates the input information of ten landslide conditioning factors (slope, aspect, relief amplitude, toposhape, topographic wetness index, distance to roads, distance to rivers, lithology, distance to faults, and rainfall) into two distinctive class labels: ‘landslide’ and ‘non-landslide’. Experimental results with a cross validation process and sensitivity analysis on the RFC model parameters reveal that the proposed model achieves a superior prediction accuracy with an area under the curve of 0.92. The RFC significantly outperforms other benchmarking methods, including discriminant analysis, logistic regression, artificial neural networks, relevance vector machines, and support vector machines. Based on our experimental outcome and comparative analysis, we strongly recommend the RFC as a very capable tool for spatial modeling of rainfall-induced landslides.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003
Althuwaynee O, Pradhan B, Park H-J, Lee J (2014) A novel ensemble decision tree-based CHi-squared automatic interaction detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063–1078. https://doi.org/10.1007/s10346-014-0466-0
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
Beale MH, Hagan MT, Demuth HB (2012) Neural network toolbox user’s guide. MathWorks, Inc., Natick
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Cascini L, Cuomo S, Guida D (2008a) Typical source areas of may 1998 flow-like mass movements in the Campania region, southern Italy. Eng Geol 96:107–125. https://doi.org/10.1016/j.enggeo.2007.10.003
Cascini L, Cuomo S, Pastor M (2008b) The role played by mountain tracks on rainfall-induced shallow landslides: a case study. In: In Proc. of the International Congress on Environmental Modelling & Software (iEMSs 2008), Barcelona, Catalonia, July 6–10, 2008. International Environmental Modelling & Software Society (iEMSs), pp 1484–1491. https://scholarsarchive.byu.edu/iemssconference/2008/
Chauhan S, Sharma M, Arora MK, Gupta NK (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. Int J Appl Earth Obs 12:340–350
Cheng M-Y, Hoang N-D (2015a) A swarm-optimized fuzzy instance-based learning approach for predicting slope collapses in mountain roads. Knowl-Based Syst 76:256–263. https://doi.org/10.1016/j.knosys.2014.12.022
Cheng M-Y, Hoang N-D (2015b) Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards 78:1961–1978. https://doi.org/10.1007/s11069-015-1813-8
Cheng M-Y, Hoang N-D (2016) Slope collapse prediction using Bayesian framework with K-nearest neighbor density estimation: case study in Taiwan. J Comput Civ Eng 30:04014116. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000456
Chung C-J, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452. https://doi.org/10.1016/j.geomorph.2006.12.036
Crozier MJ (2010) Deciphering the effect of climate change on landslide activity: a review. Geomorphology 124:260–267. https://doi.org/10.1016/j.geomorph.2010.04.009
Cuomo S, Della Sala M, Novità A (2015a) Physically based modelling of soil erosion induced by rainfall in small mountain basins. Geomorphology 243:106–115. https://doi.org/10.1016/j.geomorph.2015.04.019
Cuomo S, Della SM, Pierri M (2015b) Physically-based modeling of runoff and soil erosion in slopes with mountain tracks. In: Manzanal D, Sfriso AO (eds) In Proc. of the Pan. Conf. on Soil Mechanics and Geotechnical Engineering. IOS Press, pp 3143–3150. https://www.iospress.nl/book/from-fundamentals-to-applications-in-geotechnics/
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Feizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth 7:688–708. https://doi.org/10.1080/17538947.2012.749950
Fischer MM, Getis A, Gorsevski P, Gessler P, Jankowski P (2010) A fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. In: Handbook of applied spatial analysis. Springer, Berlin Heidelberg, pp 653–684. https://doi.org/10.1007/978-3-642-03647-7_31
Formetta G, Rago V, Capparelli G, Rigon R, Muto F, Versace P (2014) Integrated physically based system for modeling landslide susceptibility. Proc Earth Planet Sci 9:74–82. https://doi.org/10.1016/j.proeps.2014.06.006
Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27:294–300. https://doi.org/10.1016/j.patrec.2005.08.011
Glade T, Anderson M, Crozier MJ (2005) Landslide hazard and risk. Wiley, West Sussex
Ho TC (2008) Application of structural geology methods, remote sensing, and GIS for the assessment and prediction of landslide and flood along the National Road 32 in the Yen Bai and Lai Chau provinces of Vietnam, technical report. Vietnam Institute of Geosciences and Mineral Resources, Hanoi City
Ho TC et al. (2010) Combination of structural geology, remote sensing, and GIS for the study of current status and prediction of flash floods and landslides at the National Road No.32 section from the Yen Bai to the Lai Chau Provinces, technical Report. Vietnam Institude of Geosciences and Mineral Resources, Hanoi City
Hoang N-D, Pham A-D (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46:60–68. https://doi.org/10.1016/j.eswa.2015.10.020
Hoang N-D, Tien-Bui D (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civ Eng 30:04016001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000557
Hoang N-D, Tien Bui D (2018) GIS-based landslide spatial modeling using batch-training back-propagation artificial neural network: a study of model parameters. In: Tien Bui D, Ngoc Do A, Bui H-B, Hoang N-D (eds) Advances and applications in geospatial technology and earth resources: Proc Int Conf on Geo-Spatial Technologies and Earth Resources 2017. Springer International Publ AG, Cham, pp 239–254. https://doi.org/10.1007/978-3-319-68240-2_15
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
Huggel C, Clague JJ, Korup O (2012) Is climate change responsible for changing landslide activity in high mountains? Earth Surf Process Landf 37:77–91. https://doi.org/10.1002/esp.2223
Lee S, Won J-S, Jeon S, Park I, Lee M (2015) Spatial landslide hazard prediction using rainfall probability and a logistic regression. Model Math Geol 47:565–589. https://doi.org/10.1007/s11004-014-9560-z
López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141. https://doi.org/10.1016/j.ins.2013.07.007
Mandal S, Mandal K (2018) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-018-0426-0
Martinez WL, Martinez AR (2005) Exploratory data analysis with MATLAB. Chapman & Hall/CRC Press, Boca Raton
MathWorks Inc. (2015) Statistics and machine learning toolbox. The MathWorks, Inc., Natick
Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in Central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97. https://doi.org/10.1016/j.geomorph.2014.12.042
Naghibi SA, Pourghasemi HR, Dixon B (2015) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188:1–27. https://doi.org/10.1007/s10661-015-5049-6
Nguyen Q-K, Tien Bui D, Hoang N-D, Trinh P, Nguyen V-H, Yilmaz I (2017) A novel hybrid approach based on instance based learning classifier and rotation forest ensemble for spatial prediction of rainfall-induced shallow landslides using GIS. Sustainability 9:813. https://doi.org/10.3390/su9050813
Park HJ, Lee JH, 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 I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. Int J Remote Sens 35:6089–6112. https://doi.org/10.1080/01431161.2014.943326
Pham BT, Tien Bui D, Pourghasemi H, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol122(3):1–19. doi:https://doi.org/10.1007/s00704-015-1702-9
Pham BT, Tien Bui D, Prakash I, Nguyen LH, Dholakia MB (2017) A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ Earth Sci 76:371. https://doi.org/10.1007/s12665-017-6689-3
Pham BT, Tien Bui D, Prakash I (2018) Bagging based support vector machines for spatial prediction of landslides. Environ Earth Sci 77:146. https://doi.org/10.1007/s12665-018-7268-y
Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron highlands, Malaysia). IEEE Trans Geosci Remote Sens 48:4164–4177. https://doi.org/10.1109/tgrs.2010.2050328
Prasad A, Iverson L, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199. https://doi.org/10.1007/s10021-005-0054-1
Rahmati O, Pourghasemi HR, Melesse AM (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran region, Iran. Catena 137:360–372. https://doi.org/10.1016/j.catena.2015.10.010
Rianna G, Zollo A, Tommasi P, Paciucci M, Comegna L, Mercogliano P (2014) Evaluation of the effects of climate changes on landslide activity of Orvieto clayey slope. Procedia Earth Planet Sci 9:54–63. https://doi.org/10.1016/j.proeps.2014.06.017
Riedmiller M, Braun H (1993) A direct adaptive method for faster back-propagation learning: the RPROP algorithm. In: Proc of the IEEE International Conference on Neural Networks, San Francisco, CA, 28 Mar 1993–01 Apr 1993. The Institute of Electrical and Electronics Engineers, San Francisco, vol 1, pp 586–591. http://ieeexplore.ieee.org/document/298623/
Rokach L (2016) Decision forest: twenty years of research. Inform Fusion 27:111–125. https://doi.org/10.1016/j.inffus.2015.06.005
Rokach L, Maimon O (2010) Datamining and knowledge discovery handbook. Springer, New York.. https://doi.org/10.1007/978-0-387-09823-4
Santacana N, Baeze B, Corominas J, Paz AD, Marturia J (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet area (eastern Pyrenees, Spain). Nat Hazards 30:281–295
Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899. https://doi.org/10.1038/srep09899
Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199
Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577. https://doi.org/10.1016/j.rse.2011.05.013
Süzen ML, Kaya BŞ (2012) Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth 5:338–355. https://doi.org/10.1080/17538947.2011.586443
Tien Bui D, Ho CT, Revhaug I (2012a) GIS-based landslide susceptibility assessment along the National road 32 (Vietnam) using logistic regression and index of entropy models In: Proc Int Symp on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, 16–20 October 2012, Ho Chi Minh City, Vietnam. Ho Chi Minh City University of Technology and Ho Chi Minh City Institute of Resources Geography, Ho Chi Minh City. http://gisws.media.osaka-cu.ac.jp/gisideas12/
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40. https://doi.org/10.1016/j.catena.2012.04.001
Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen D (2014) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D (eds) Cartography from pole to pole. Lecture Notes in geoinformation and cartography. Springer, Berlin Heidelberg, pp 303–317. https://doi.org/10.1007/978-3-642-32618-9
Tien Bui D, Tran AT, 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(2):361–378. https://doi.org/10.1007/s10346-015-0557-6
Tien Bui D, Ho T-C, Pradhan B, Pham B-T, Nhu V-H, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining based functional trees classifier with AdaBoost, bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75:1101–1123
Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244. https://doi.org/10.1162/15324430152748236
Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74(3): 1489–1516. https://doi.org/10.1007/s11069-014-1245-x
van Erkel AR, Pattynama PMT (1998) Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 27:88–94. https://doi.org/10.1016/S0720-048X(97)00157-5
Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forests: a survey and results of new tests. Pattern Recogn 44:330–349. https://doi.org/10.1016/j.patcog.2010.08.011
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
Acknowledgements
Data for this research are from the project 71 /GV-VKHĐCKS with the title “Combination of Structural Geology, Remote Sensing, and GIS for the Study of Current Status and Prediction of Flash Floods and Landslides at the National Road No.32 Section from the Yen Bai to the Lai Chau Provinces”, Vietnam Institude of Geosciences and Mineral Resources. We would like to thank Dr. Ho Tien Chung for providing the data for this research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dang, VH., Dieu, T.B., Tran, XL. et al. Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bull Eng Geol Environ 78, 2835–2849 (2019). https://doi.org/10.1007/s10064-018-1273-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10064-018-1273-y