Landslide susceptibility mapping using Partial Decision Tree-Based hybrid artificial intelligence models
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https://doi.org/10.15625/2615-9783/24023Keywords:
Machine learning, landslide susceptibility, ensemble models, Partial Decision Trees, Decorate, PART, VietnamAbstract
In this research, two newly hybrid machine learning (ML) models, including Decorate Ensemble-based Partial Decision Trees (D-PART) and Bagging Ensemble-based Partial Decision Trees (B-PART), were applied to generate an accurate landslide susceptibility map for the Muong Te area, Lai Chau Province, Vietnam. The performance of the novel models was compared with two single benchmark models, namely Partial Decision Trees (PART) and Logistic Regression (LR), using the popular area under the Receiver Operating Characteristic (ROC) curve (AUC) metric. To construct the training and validation datasets, a spatial database was developed comprising ten landslide conditioning factors associated with the area's topographic, geological, structural, and hydrological characteristics, along with 248 documented historical and recent landslide occurrences. The OneR technique was applied to prioritize the most influential factors and to improve the model's performance. The evaluation results demonstrate that D-PART yielded the strongest predictive performance, with an AUC of 0.801, followed by B-PART (0.795), PART (0.758), and Logistic Regression (0.736). Thus, the novel hybrid model D-PART is a promising technique for constructing a reliable landslide susceptibility map, which can be used for effective planning and management of landslides in landslide-prone areas.
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