Assessment of Landslide Susceptibility in Jecheon Using Deep Learning Based on Exploratory Data Analysis

Sang-A Ahn; Jung-Hyun Lee; Hyuck-Jin Park

doi:10.9720/kseg.2023.4.673

All Issue

2023 Vol.33, Issue 4 Preview Page Next Page

Research Article

Assessment of Landslide Susceptibility in Jecheon Using Deep Learning Based on Exploratory Data Analysis 데이터 탐색을 활용한 딥러닝 기반 제천 지역 산사태 취약성 분석

31 December 2023. pp. 673-687

PDF XML

Abstract

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model’s performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

Keywords

landslide susceptibility

exploratory data analysis

correlation coefficient

feature importance

multi-layer perceptron

데이터 탐색은 수집한 데이터를 다양한 각도에서 관찰 및 이해하는 과정으로 데이터 구조 및 특성 분석을 통해 데이터의 분포와 상관관계를 파악하는 과정이다. 일반적으로 산사태는 다양한 인자들에 의해 유발되고 발생 지역에 따라 유발 인자들이 미치는 영향이 상이하기 때문에 산사태 취약성 분석 이전에 데이터 탐색을 통해 유발 인자 사이의 상관관계를 파악하고 특징적인 유발 인자를 선별한다면 효과적인 분석을 수행할 수 있다. 따라서 본 연구는 데이터 탐색이 예측 모델의 성능에 미치는 결과를 확인하기 위해 두 단계에 걸친 데이터 탐색을 수행하여 인자를 선별하고, 선별된 유발 인자들 사이의 조합과 23개의 전체 유발 인자 조합을 활용하여 딥러닝 기반의 산사태 취약성 분석을 진행하였다. 데이터 탐색 과정에서는 Pearson 상관계수 heat map과 random forest의 인자 중요도 histogram을 활용하였으며, 딥러닝 기반 산사태 취약성 분석 결과의 정확도는 분석을 통해 획득한 산사태 취약 지수 값을 이용해 제작한 산사태 취약성 지도를 confusion matrix 기반의 정확도 검증 방법을 통해 분석하였다. 분석 결과, 전체 23개의 인자를 사용한 산사태 취약성 해석 결과는 55.90%의 낮은 정확도를 보였지만 한 단계의 탐색을 거쳐 선별한 13개 인자를 활용한 취약성 해석 결과는 81.25%의 분석 정확도를 보였고, 두 단계 데이터 탐색을 모두 수행하여 선별된 9개의 유발 인자를 활용한 산사태 취약성 분석 결과는 92.80%로 가장 높은 정확도를 보였다. 따라서 데이터 탐색을 통해 특징적인 유발 인자를 선별하고 분석에 활용하는 것이 산사태 취약성 분석에서 더 좋은 분석 성능을 기대할 수 있음을 확인하였다.

키워드

산사태 취약성

데이터 탐색

상관계수

인자 중요도

다층 퍼셉트론

References

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

Information

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
DOI :https://doi.org/10.9720/kseg.2023.4.673

The Journal of Engineering GeologyISSN:1226-5268(Print) 2287-7169(Online)대한지질공학회

All Issue