All Issue

2023 Vol.33, Issue 4 Preview Page

Research Article

31 December 2023. pp. 673-687
Biau, G., Scornet, E., 2016, A random forest guided tour, Test, 25, 197-227. 10.1007/s11749-016-0481-7
Breiman, L., 2001, Random forests, Machine Learning, 45, 5-32. 10.1023/A:1010933404324
Brownley, C.W., 2016, Foundations for analytics with Python: From non-programmer to hacker, O'Reilly Media, 352p.
Bui, D.T., Lofman, O., Revhaug, I., Dick, O., 2011, Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression, Natural hazards, 59, 1413-1444. 10.1007/s11069-011-9844-2
Géron, A., 2022, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, Inc., 864p.
Gomez, H., Kavzoglu, T., 2005, Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Engineering Geology, 78(1-2), 11-27. 10.1016/j.enggeo.2004.10.004
Hauke, J., Kossowski, T., 2011, Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data, Quaestiones Geographicae, 30(2), 87-93. 10.2478/v10117-011-0021-1
Huh, M.H., Jung, J.J., 1990, Software review of statistical package programs on EDA aspects, The Korean Journal of Applied Statistics, 3(2), 17-25 (in Korean with English abstract).
Kääb, A., 2002, Monitoring high-mountain terrain deformation from repeated air- and spaceborne optical data: Examples using digital aerial imagery and ASTER data, ISPRS Journal of Photogrammetry and Remote Sensing, 57(1-2), 39-52. 10.1016/S0924-2716(02)00114-4
Kalantar, B., Pradhan, B., Naghibi, S.A., Motevalli, A., 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, 9(1), 49-69. 10.1080/19475705.2017.1407368
Kang, K.H., Park, H.J., 2019, Study on the effect of training data sampling strategy on the accuracy of the landslide susceptibility analysis using random forest method, Economic and Environmental Geology, 52(2), 199-212 (in Korean with English abstract). 10.9719/EEG.2019.52.2.199
Lee, J.H., Kim, H., Park, H.J., Heo, J.H., 2021, Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution, Landslides, 18, 321-338. 10.1007/s10346-020-01502-7
Lee, J.N., Kim, T.S., 2006, Statistics (with R), Freeacademy Inc., 456p.
Lee, S., Oh, H.J., 2019, Landslide susceptibility prediction using evidential belief function, weight of evidence and artificial neural network models, Korean Journal of Remote Sensing, 35(2), 299-316 (in Korean with English abstract). 10.7780/kjrs.2019.35.2.9
Liaw, A., Wiener, M., 2002, Classification and regression by randomForest, R News, 2, 18-22.
Martha, T.R., N., Kerle, V., Jetten, C.J., Van Westen, K., Vinod Kumar, 2010, Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, Geomorphology, 116(1-2), 24-36. 10.1016/j.geomorph.2009.10.004
Muller, A.C., Guido, S., 2016, Introduction to machine learning with Python: A guide for data scientists, O'Reilly Media, Inc., 386p.
Park, H.J., Lee, J.H., 2022, A review of quantitative landslide susceptibility analysis methods using physically based modelling, The Journal of Engineering Geology, 32(1), 27-40 (in Korean with English abstract). 10.9720/kseg.2022.1.027
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, 361-378. 10.1007/s10346-015-0557-6
Xiao, C., Ye, J., Esteves, R.M., Rong, C., 2016, Using Spearman's correlation coefficients for exploratory data analysis on big dataset, Concurrency and Computation: Practice and Experience, 28(14), 3866-3878. 10.1002/cpe.3745
  • Publisher :Korean Society of Engineering Geology
  • Publisher(Ko) :대한지질공학회
  • Journal Title :The Journal of Engineering Geology
  • Journal Title(Ko) :지질공학
  • Volume : 33
  • No :4
  • Pages :673-687
  • Received Date : 2023-12-12
  • Revised Date : 2023-12-26
  • Accepted Date : 2023-12-26