Fig. 1. Building process of Random Forest.
Fig. 2. Study area: (a) location of Sangju area, (b) shaded relief map of Sangju area with landslide locations, (c) location of Jinbu area, (d) shaded relief map of Jinbu area with landslide locations.
Fig. 3. Thematic maps for Sangju area: (a) altitude, (b) slope angle, (c) aspect, (d) standard curvature, (e) plan curvature, (f) profile curvature, (g) TWI, (h) SPI, (i) geology, (j) distance from faults, (k) forest type, (l) timber age, (m) timber diameter, (n) forest density, (o) soil type.
Fig. 4. Thematic maps for Jinbu area: (a) altitude, (b) slope angle, (c) aspect, (d) standard curvature, (e) plan curvature, (f) profile curvature, (g) TWI, (h) SPI, (i) geology, (j) distance from faults, (k) forest type, (l) timber age, (m) timber diameter, (n) forest density, (o) soil type.
Fig. 5. Methodological flow chart of the research process.
Fig. 6. Landslide susceptibility maps for Sangju area: (a) Case 1, (b) Case 2, (c) Case 3, (d) Case 4, (e) Case 5, (f) Case 6.
Fig. 7. Landslide susceptibility maps for Jinbu area: (a) Case 1, (b) Case 2, (c) Case 3, (d) Case 4, (e) Case 5, (f) Case 6.
Fig. 8. Prediction rate curve for landslide susceptibility maps for Sangju area.
Fig. 9. Prediction rate curve for landslide susceptibility maps for Jinbu area.
Table 1. The landslide conditioning factors used in this study
Table 2. Training scenarios according to the sampling strategy
Table 3. Prediction rate of landslide susceptibility maps
참고문헌
- Baeza, C., Lantada, N. and Moya, J. (2010) Influence of sample and terrain unit on landslide susceptibility assessment at La Pobla de Lillet, Eastern Pyrenees, Spain. Environmental Earth Sciences, v.60, p.155-167. https://doi.org/10.1007/s12665-009-0176-4
- Breiman, L. (2001) Random forest. Machine Learning, v.45, p.5-32. https://doi.org/10.1023/A:1010933404324
- Brenning, A. (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Science, v.5, p.853-862. https://doi.org/10.5194/nhess-5-853-2005
- Catani, F., Lagomarsino, D., Segoni, S. and Tofani, V. (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Natural Hazards and Earth System Sciences, v.13, p.2815-2831. https://doi.org/10.5194/nhess-13-2815-2013
- Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T., Duan, Z. and 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, v.151, p.147-160. https://doi.org/10.1016/j.catena.2016.11.032
- Chen, W., Zhang, S., Li, R. and Shahabi, H. (2018) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive Bayes tree for landslide susceptibility modeling. Science of the Total Environment, v.644, p.1006-1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
- Cho, J.H. and Kurup, P.U. (2011) Decision tree approach for classification and dimensionality reduction of electronic nose data. Sensors and Actuators B: Chemical, v.160, p.542-548. https://doi.org/10.1016/j.snb.2011.08.027
- Chung, C.J.F. and Fabbri, A.G. (2003) Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, v.30, p.451-472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
- Dittman, D. J., Khoshgoftaar, T. M. and Napolitano, A. (2015). The effect of data sampling when using random forest on imbalanced bioinformatics data. In: 2015 IEEE International Conference on Information Reuse and Integration (IRI), pp. 457-463.
- Dudoit, S., Fridlyand, J. and Speed, T.P. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American statistical association, v.97, p.77-87. https://doi.org/10.1198/016214502753479248
- Duro, D.C., Franklin, S.E. and Dube, M.G. (2012) Multiscale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing, v.33, p.4502-4526. https://doi.org/10.1080/01431161.2011.649864
- Goetz, J.N., Brenning, A., Petschko, H. and Leopold, P. (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences, v.81, p.1-11. https://doi.org/10.1016/j.cageo.2015.04.007
- Guzzetti, F., Carrara, A., Cardinali, M. and Reichenbach, P. (1999) Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology, v.31, p.181-216. https://doi.org/10.1016/S0169-555X(99)00078-1
- Hamza, M. and Larocque, D. (2005) An empirical comparison of ensemble methods based on classification trees. Journal of Statistical Computation and Simulation, v.75, p.629-643. https://doi.org/10.1080/00949650410001729472
- Hong, H., Pourghasemi, H.R. and Pourtaghi, Z.S. (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, v.259, p.105-118. https://doi.org/10.1016/j.geomorph.2016.02.012
- Hong, H., Pradhan, B., Xu, C. and Bui, D.T. (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, v.133, p.266-281. https://doi.org/10.1016/j.catena.2015.05.019
- Kalantar, B., Pradhan, B., Naghibi, S.A., Motevalli, A. and Mansor, S. (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, v.9, p.49-69. https://doi.org/10.1080/19475705.2017.1407368
- Kim, J.C., Lee, S., Jung, H.S. and Lee, S. (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto International, v.33, p.1000-1015. https://doi.org/10.1080/10106049.2017.1323964
- Kim, W.Y., Chae B.G., Kim, K.S., Cho, Y.C., Lee, C.O., Lee, C.W., Kim, K.Y., Kim, J.H. and Kim, J.M. (2003) Study on landslide hazard prediction. Ministry of Science and Technology, 339p.
- Lee S., Lee M.J. and Won J.S. (2005) Landslide susceptibility analysis and verification using artificial neural network in the Kangneung area. Economic and Environmental Geology, v.38, p.1-11.
- Lee, J.H. and Park, H.J. (2012) Assessment of landslide susceptibility using a coupled infinite slope model and hydrologic model in Jinbu area, Gangwon-do. Economic and Environmental Geology, v.45, p.697-707. https://doi.org/10.9719/EEG.2012.45.6.697
- Liaw, A. and Wiener, M. (2002) Classification and regression by randomForest. R news, v.2, p.18-22.
- Muller, A.C. and Guido, S. (2016) Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, Inc., 386p.
- Myles, A.J., Feudale, R.N., Liu, Y., Woody, N.A. and Brown, S.D. (2004) An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, v.18, p.275-285. https://doi.org/10.1002/cem.873
- Na, X., Zhang, S., Li, X., Yu, H. and Liu, C. (2010) Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. Photogrammetric Engineering & Remote Sensing, v.76, p.833-840. https://doi.org/10.14358/PERS.76.7.833
- Paola, J. D. and Schowengerdt, R. A. (1995) A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of remote sensing, v.16, p.3033-3058. https://doi.org/10.1080/01431169508954607
- Park, C. (2016) A simple diagnostic statistic for determining the size of random forest. Journal of the Korean Data and Information Science Society, v.27, p.855-863. https://doi.org/10.7465/jkdi.2016.27.4.855
- Park, C. (2017) A measure of discrepancy based on margin of victory useful for the determination of random forest size. The Korean Data & Information Science Society, v.28, p.515-524.
- Pham, B.T., Bui, D.T., Prakash, I. and Dholakia, M.B. (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena, v.149, p.52-63. https://doi.org/10.1016/j.catena.2016.09.007
- Pham, B.T., Pradhan, B., Bui, D.T., Prakash, I. and Dholakia, M.B. (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environmental Modelling & Software, v.84, p.240-250. https://doi.org/10.1016/j.envsoft.2016.07.005
- Pradhan, B. (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, v.51, p.350-365. https://doi.org/10.1016/j.cageo.2012.08.023
- Pradhan, B. and 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. Environmental Modelling & Software, v.25, p.747-759. https://doi.org/10.1016/j.envsoft.2009.10.016
- Stumpf, A. and Kerle, N. (2011) Object-oriented mapping of landslides using Random Forests. Remote sensing of environment, v.115, p.2564-2577. https://doi.org/10.1016/j.rse.2011.05.013
- Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I. and Dick, O.B. (2012) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. Proceedings of the iEMSs Sixth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2012). International Environmental Modelling and Software Society, Leipzig, Germany(July).
- Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I. and Dick, O.B. (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Natural hazards, v.66, p.707-730. https://doi.org/10.1007/s11069-012-0510-0
- Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B. and 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, v.13, p.361-378. https://doi.org/10.1007/s10346-015-0557-6
- Tsangaratos, P. and Ilia, I. (2016) Comparison of a logistic regression and Naive Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena, v.145, p.164-179. https://doi.org/10.1016/j.catena.2016.06.004
- Watts, J.D., Lawrence, R.L., Miller, P.R. and Montagne, C. (2009) Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing. Remote Sensing of Environment, v.113, p.1843-1852. https://doi.org/10.1016/j.rse.2009.04.015
- Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., MacLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D.J. and Steinberg, D. (2008) Top 10 algorithms in data mining. Knowledge and information systems, v.14, p.1-37. https://doi.org/10.1007/s10115-007-0114-2
- Yilmaz, I. (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environmental Earth Sciences, v.60, p.505-519. https://doi.org/10.1007/s12665-009-0191-5
- Zhang, K., Wu, X., Niu, R., Yang, K. and Zhao, L. (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environmental Earth Sciences, v.76, p.405. https://doi.org/10.1007/s12665-017-6731-5