Abstract
Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.
Highlights
Landslides are one of the most common natural hazards and when they occur, they usually cause loss of life and significant economic losses [1,2]
After using Bayesian optimization to adjust the parameters, random forest (RF) model accuracy improved by 1%, while the gradient boosting decision tree (GBDT) model improved by 7% and the GBDT_B accuracy is the highest of the four models and the AUC increased to the highest 0.866
This study presented the application of Bayesian optimization (BO) in RF and GBDT models for Landslide susceptibility mapping (LSM) in Shuicheng
Summary
Landslides are one of the most common natural hazards and when they occur, they usually cause loss of life and significant economic losses [1,2]. How to assess the risk of landslides effectively. Risk is composed of the hazard of disaster, the vulnerability and exposure of victims and the disaster preparedness and mitigation capacity. The hazard is composed of the susceptibility and probability of inducing factors. The susceptibility assessment is the key point of the risk assessment. Landslide susceptibility mapping (LSM) is a very excellent approach of susceptibility assessment because it can provide information on the potential area of landslides occurrence [4,5,6]. The core supposition of LSM is that future landslides more likely to occur under the same or similar environmental conditions as previous hazards [7]. LSM can predict the potential area of future hazards occurrence by considering the historical disaster locations and their conditioning factors. The results of LSM may be affected by prediction models, which means that it is very important to choose suitable research methods for ensuring the availability and scientific validity of the LSM
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