Author
Listed:
- Huimin Liu
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, 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
Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, 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
Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China)
- Rong Gui
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)
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.
Suggested Citation
Huimin Liu & Qixuan Ding & Xuexi Yang & Qinghao Liu & Min Deng & Rong Gui, 2024.
"A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning,"
Sustainability, MDPI, vol. 16(11), pages 1-22, May.
Handle:
RePEc:gam:jsusta:v:16:y:2024:i:11:p:4547-:d:1403047
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Citations
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Cited by:
- Ahmed M. Youssef & Bosy A. El‑Haddad & Hariklia D. Skilodimou & George D. Bathrellos & Foroogh Golkar & Hamid Reza Pourghasemi, 2024.
"Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping,"
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14227-14258, December.
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