Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou...

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Bibliographic Details
Published in:Acta geochimica 2019-10, Vol.38 (5), p.654-669
Main Authors: Xiao, Ting, Yin, Kunlong, Yao, Tianlu, Liu, Shuhao
Format: Article
Language:English
Subjects:
Aerial surveys
Artificial intelligence
Canyons
Coefficients
Correlation analysis
Earth and Environmental Science
Earth Sciences
Entropy
Environmental risk
Geochemistry
Geographical information systems
Landslides
Learning algorithms
Machine learning
Mapping
Mathematical models
Model accuracy
Original Article
Risk management
Statistical analysis
Statistical models
Statistical prediction
Surveys
Training
Urban planning
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Summary:Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.
ISSN:2096-0956
2365-7499
DOI:10.1007/s11631-019-00341-1