A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

Landslide sensitivity prediction relies on multiple environmental factors, making it difficult to obtain accurate prediction results. In order to improve the prediction accuracy of regional landslide sensitivity, a landslide sensitivity spatial distribution prediction method based on integrated particle swarm optimization was studied in Lianhe Village, Jianfeng Town, Shizhong District, Leshan City, Sichuan Province. Based on the determination coefficient, the sensitivity of landslide influencing factors was analyzed, and the weights of the influencing factors were determined. A landslide sensitivity spatial distribution prediction model was established based on support vector machine. By introducing simulated annealing and mutation operations into the particle swarm algorithm, an integrated particle swarm algorithm was obtained to extract high weight features of landslide sensitivity space and generate landslide sensitivity prediction results. The experimental results show that the cumulative value (ACU) of this method for predicting landslide sensitivity is 0.91, which can accurately predict the spatial distribution of landslide sensitivity in the study area and has practical value.

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A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning

Liu,

Ding,

Yang

et al. 2024

Sustainability

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Landslide susceptibility mapping (LSM) constitutes a valuable analytical instrument for estimating the likelihood of landslide occurrence, thereby furnishing a scientific foundation for the prevention of natural hazards, land-use planning, and economic development in landslide-prone areas. Existing LSM methods are predominantly data-driven, allowing for significantly enhanced monitoring accuracy. However, these methods often overlook the consideration of landslide mechanisms and uncertainties associated with non-landslide samples, resulting in lower model reliability. To effectively address this issue, a knowledge-guided landslide susceptibility assessment framework is proposed in this study to enhance the interpretability and monitoring accuracy of LSM. First, a landslide knowledge graph is constructed to model the relationships between landslide entities and summarize landslide susceptibility rules. Next, combining the obtained landslide rules with geographic similarity principles, high-confidence non-landslide samples are selected to optimize the quality of the samples. Subsequently, a Landslide Knowledge Fusion Cell (LKF-Cell) is utilized to couple landslide data with landslide knowledge, resulting in the acquisition of informative and semantically rich landslide event features. Finally, a precise and credible landslide susceptibility assessment model is built based on a convolutional neural network (CNN), and landslide susceptibility spatial distribution levels are mapped. The research findings indicate that the CNN-based model outperforms traditional machine learning algorithms in predicting landslide probability; in particular, the Area Under the Curve (AUC) of the model was improved by 3–6% after sample optimization, and the AUC value of the LKF-Cell method was 6–11% higher than the baseline method.

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A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

Cited by 3 publications

References 106 publications

Mo-BAPER: A Modified TabNet Employing Global Average Pooling for Bogor Area Landslide Prediction

Mo-BAPER: A Modified TabNet Employing Global Average Pooling for Bogor Area Landslide Prediction

Spatial distribution prediction of landslide susceptibility based on integrated particle swarm optimization

A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning

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