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

Huimin Liu, Qixuan Ding, Xuexi Yang, Qinghao Liu, Min Deng and Rong Gui ()
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Huimin Liu: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Qixuan Ding: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Xuexi Yang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Qinghao Liu: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Min Deng: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Rong Gui: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Sustainability, 2024, vol. 16, issue 11, 1-22

Abstract: 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.

Keywords: landslide susceptibility mapping; knowledge graph; convolutional neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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