FR-weighted GeoDetector for landslide susceptibility and driving factors analysis

Abstract

Landslide susceptibility analysis is an essential tool for landslide hazard management. Correlation analysis of the driving factors before landslide susceptibility analysis is crucial to obtain more accurate results and higher computational efficiency. This article presents an FR-weighted GeoDetector, which can, at different gridding scales, stably screen out the driving factors most relevant to historical landslides in the study area compared to the performance of the original GeoDetector. The correlation analysis result shows that the most relevant seven conditioning factors to historical landslides in the study area are: lithology, distance to road, elevation, slope, STI, SPI, and distance to faults. Four machine learning models (logistic regression [LR], random forest [RF], artificial neural network [ANN], and Xgboost) are implemented for landslide susceptibility analysis, demonstrating that such models can achieve higher accuracy with features filtered by the FR-weighted GeoDetector than with all features. The Xgboost models trained on seven and 12 features were used to generate landslide susceptibility maps. The overlay with historical landslides showed that the models trained on seven features generated a more reasonable landslide susceptibility map, proving that selecting crucial landslide conditioning factors is a better solution than using a full range of landslide conditioning factors.

Keywords:

• Landslide susceptibility
• GeoDetector
• correlation analysis
• driving factors

Acknowledgements

The authors would like to thank the fund providers, the anonymous reviewers, and the editors of the journal for constructive comments and suggestions.

Disclosure statement

The authors report no conflict of interest.

Data availability statement

Datasets used in this study are available from the corresponding author on reasonable request.

Additional information

Funding

This research was funded by the Hong Kong Polytechnic University, grant number ZVSN and Smart Cities Research Institute, The Hong Kong Polytechnic University, grant number CD03.