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지질조사와 자원연구를 선도하는 한국지질자원연구원 korea institute of geoscience and mineral resouces

논문 상세정보

논문 상세정보
학회/학술지명 REMOTE SENSING 주저자 디엔티엔부이
발표매체 SCI Expanded 국가명 스위스
논문명 국문 Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
영문 Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
발표일 2019.04.30
관련과제 디지털매핑에 의한 통합 지질정보 제공기술 개발 바로가기
초록 We used a novel hybrid functional machine learning algorithm to predict the spatial
distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model
which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost
(AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates
20 landslide conditioning factors, which we ranked using the least-square support vector machine
(LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were
Remote Sens. 2019, 11, 931; doi:10.3390/rs11080931 www.mdpi.com/journal/remotesensing
Remote Sens. 2019, 11, 931 2 of 22
used for training and 30% (19) for validation processes. Model validation was performed using
sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver
operatic characteristic (AUC) curve. We also used soft computing benchmark models, including
SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model
validation and comparison. The selected conditioning factors were significant in landslide occurrence
but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860)
outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm
that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces
noise and enhan

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