Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models

Bibliographic Data

ID22031797
AuthorsFaming Huang (0000-0002-4428-7133, Nanchang University), Jiawu Chen (Nanchang University), Zhen Du (0000-0001-8359-6118, Nanchang University), Chi Yao (0000-0002-7747-7320, Nanchang University, corresponding author), Jinsong Huang (0000-0002-5159-1635, ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals), Qinghui Jiang (0000-0002-6511-8208, Nanchang University), Zhilu Chang (0000-0001-7744-4548, Nanchang University), Shu Li (0000-0003-4402-1674, Changjiang Institute of Survey, Planning, Design and Research)
Year2020
Volume9
Issue6
Pages377
Publication date2020-06-08
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9060377
OpenAlexW3035405765
LanguageEN
Citations received4
References cited74

Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions—C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)—are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models; C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor; (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions; and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models

Artificial neural network · Data mining · Decision tree · Erosion · Geomorphology · Geotechnical engineering · Landslide · Logistic regression · Machine learning · Multilayer perceptron · Perceptron · Predictive modelling · Receiver operating characteristic · Support vector machine · Computer Science · Dam Engineering and Safety · Landslides and related hazards · Soil erosion and sediment transport · Artificial Intelligence · Geology

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