[논문]머신러닝기법을 이용한 산사태 발생인자의 영향도 분석

박성용; 문성우; 최재완; 서용석

doi:10.9720/kseg.2021.4.701

머신러닝기법을 이용한 산사태 발생인자의 영향도 분석
Machine-Learning Evaluation of Factors Influencing Landslides 원문보기

지질공학 = The journal of engineering geology, v.31 no.4, 2021년, pp.701 - 718

박성용 (충북대학교 지구환경과학과) , 문성우 (충북대학교 지구환경과학과) , 최재완 (충북대학교 토목공학과) , 서용석 (충북대학교 지구환경과학과)

초록
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본 연구에서는 산사태가 다수 발생한 충주 산척면 지역을 대상으로 야외지질조사 및 일련의 실내시험을 수행하여 데이터를 취득하고, 이후 인공신경망(Artificial neural network)과 로지스틱 회귀분석(Logistic regression)을 적용하여 각 인자가 산사태 발생에 미치는 영향도를 분석하였다. 야외지질조사 시 산사태 발생 유무에 따라 불교란시료를 채취하였으며, 동적 콘 관입시험기를 이용하여 토심을 측정하였다. 실내시험은 미국 표준시험법인 ASTM 규정에 따라 진행되었으며, 인자간 다중공선성을 해결하기 위해 VIF(Variation inflation factor)를 산정하였다. 다중공선성 분석을 통해 총 9개 인자(전단강도, 암종, 토심, 포화함수비, 비중, 투수계수, USCS, 사면 경사, 고도)가 분석에 적용되었다. 추후 도출되는 각 인자별 영향도를 직접적으로 비교하기 위해서 데이터는 최소값 0, 최대값 1이 되도록 최소-최대 정규화한 후 로지스틱 회귀분석 및 인공신경망 분석에 적용되었다. 로지스틱 회귀분석 결과, 토심, 경사, 포화함수비, 전단강도 순으로 산사태 발생에 영향력이 크게 나타났으며, 인공신경망 분석 결과, 경사, 토심, 포화함수비, 전단강도 순으로 영향력이 크게 나타났다. 각 분석기법으로 산정된 영향도를 산술평균한 결과, 토심, 경사, 포화함수비, 전단강도가 상위 4개 인자로 선정되었으며, 이들의 영향도 합계는 약 70%로 분석되었다.

Abstract ▼ AI-Helper

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

주제어

표/그림 (18)

그림 Fig. 1. Photographs of landslides in the (a) upper and (b) lower slope of the forest road.
그림 Fig. 2. Simplified geological map of the study area showing sampling point in accordance with forest road (modified from Park and Yeo, 1971).
그림 Fig. 3. In-situ and laboratory tests for data acquisition.
그림 Fig. 4. Distribution ranges of measured data. Categorical data including landslide occurrence, lithology, and USCS are shown as a histogram, and continuous data are shown as a box plot. Ocrn*: Occurrence
그림 Fig. 5. Distribution of dependent variable after logistic regression.
표 Table 1. Confusion matrix used to compare predicted and actual values
그림 Fig. 6. Schematic diagram of receiver operating characteristic (ROC) curve (modified from Šimundić, 2009).
표 Table 2. Diagnostic accuracy in accordance with the area under the ROC curve (Šimundić, 2009)
표 Table 3. VIF for 13 factors influencing landslides to check multicollinearity (first step)
표 Table 4. VIF for 9 factors influencing landslides to check multicollinearity (first step)
그림 Fig. 7. Map of the study area showing distribution of training and test data.
그림 Fig. 8. ROC and AUC graphs of logistic regression analysis.
표 Table 5. Results of logistic regression analysis
표 Table 6. AUC of models for selecting the number of nodes in the hidden layer
표 Table 7. AUC of models for selecting the loss function and learning rate
그림 Fig. 9. ROC and AUC graphs of selected artificial neural network analysis.
표 Table 8. Results of artificial neural network analysis
표 Table 9. Rank of landslide factors by arithmetical average of values from logistic regression analysis and artificial neural network analysis

AI 본문요약
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제안 방법

본 연구에서는 현장조사 및 실내시험을 통해 지질 및 지형 데이터를 취득하였으며, 인자별 VIF 값 산정을 통해 다중공선성 문제를 해결하여 총 9개의 인자들(암종, 토심, 고도, 경사, 포화함수비, 비중, USCS, 투수계수, 전단강도)을 분석에 적용하였다. 해당 인자들을 이용하여 로지스틱 회귀분석과 인공신경망 분석을 수행하였으며, 두 분석기법의 결과를 종합적으로 고려하여 인자별 산사태 영향도를 파악하고 주요 산사태 영향인자를 산정하였다.
본 연구에서는 현장조사와 실내시험을 통해 취득한 산사태 영향인자들을 대상으로 로지스틱 회귀분석과 인공신경망 분석을 적용하였으며, 각 인자별 영향도를 산정하고 주요 영향인자를 결정하였다. 단계별 분석 과정을 요약하면 아래와 같다.
본 연구에서는 현장조사 및 실내시험을 통해 지질 및 지형 데이터를 취득하였으며, 인자별 VIF 값 산정을 통해 다중공선성 문제를 해결하여 총 9개의 인자들(암종, 토심, 고도, 경사, 포화함수비, 비중, USCS, 투수계수, 전단강도)을 분석에 적용하였다. 해당 인자들을 이용하여 로지스틱 회귀분석과 인공신경망 분석을 수행하였으며, 두 분석기법의 결과를 종합적으로 고려하여 인자별 산사태 영향도를 파악하고 주요 산사태 영향인자를 산정하였다.

대상 데이터

(1) 2020년 8월 3일 일강우량 259 mm로 인해 산사태가 다수 발생한 충주시 산척면 송강리 일대를 대상으로 야외지질조사 및 실내시험을 통해 13개의 산사태 발생 영향인자(암종, USCS, 토심, 포화함수비, 비중, 전단강도, 공극비 및 공극률, 단위중량, 투수계수, 고도, 경사)에 대한 데이터를 획득하였다.
연구지역은 중생대 쥐라기의 청주 화강암류와 시대미상의 천등산층 함자철석 미립편마암이 주를 이루고 있다(Fig. 2).
취득한 데이터 61개 중 20개를 무작위로 선별하여 검증 데이터로 활용하였으며, 나머지 41개의 데이터는 머신러닝 기법의 학습용 데이터로 이용하였다. 학습 데이터는 산사태 발생 25개 및 미발생 16개이며, 검증 데이터는 산사태 발생 10개 및 미발생 10개로 구분된다.
취득한 데이터 61개 중 20개를 무작위로 선별하여 검증 데이터로 활용하였으며, 나머지 41개의 데이터는 머신러닝 기법의 학습용 데이터로 이용하였다. 학습 데이터는 산사태 발생 25개 및 미발생 16개이며, 검증 데이터는 산사태 발생 10개 및 미발생 10개로 구분된다. 학습 데이터와 검증데이터의 분포는 Fig.

데이터처리

예측의 정확도 평가를 위해 오차행렬의 결과로부터 ROC(Receiver operating characteristic) 곡선을 그린 후, AUC(Area under curve)를 산정하여 모형의 정확도를 평가한다. 산사태 발생에 대한 예측 성공 비율은 민감도(Sensitivity), 산사태 미발생의 예측 성공비율은 특이도(Specificity)라고 하며, 식 (7)을 통해 산정된다.

성능/효과

(2) 취득한 데이터들의 다중공선성 문제를 해결하기 위해 VIF 값이 높은 인자들을 단계별로 하나씩 제거하였으며, 총 9개 인자(전단강도, 암종, 토심, 포화함수비, 비중, 투수계수, USCS, 경사, 고도)가 로지스틱 회귀분석 및 인공신경망 분석에 적용되었다.
(3) 로지스틱 회귀분석 결과, 영향도는 토심(31.82%), 경사(28.67%), 포화함수비(13.68%), 전단강도(13.35%) 순으로 나타났으며, 도출된 모델에 대한 검증 결과 AUC 값이 0.75로 나타나 양호한(Good) 것으로 판단된다.
(4) 인공신경망 분석 및 가중치분할법 결과, 영향도는 경사(15.67%), 토심(12.96%), 포화함수비(11.69%), 전단강도 (11.22%) 순으로 나타났으며, 도출된 모델에 대한 검증 결과 AUC 값이 0.88로 나타나 매우 양호한(Very good) 것으로 판단된다.
(5) 각 분석 기법으로 도출된 영향도를 산술평균한 결과, 영향도는 토심(22.39%), 경사(22.17%), 포화함수비(12.68%), 전단강도(12.28%) 순으로 나타났으며, 해당 인자들의 영향도 합계는 약 70%로 분석되어 이들이 연구지역에서 발생한 산사태에 대한 주요 영향인자임을 확인하였다.

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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