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Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China
- Source :
- Acta Geochimica. 38:654-669
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- 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.
- Subjects :
- Geotechnical investigation
Receiver operating characteristic
business.industry
Statistical model
Landslide
Landslide susceptibility
Machine learning
computer.software_genre
Random forest
Geochemistry and Petrology
Entropy (information theory)
Artificial intelligence
Spatial prediction
business
computer
Mathematics
Subjects
Details
- ISSN :
- 23657499 and 20960956
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- Acta Geochimica
- Accession number :
- edsair.doi...........c13d7062963f8e1649d722a6f322ceec