Towards intelligent landslide susceptibility evaluation: Knowledge extraction and rule mining

Landslide susceptibility evaluation (LSE) plays a crucial role in disaster prevention and mitigation. However, current models struggle to achieve an optimal balance among accuracy, interpretability, and scalability. This study proposes a knowledge extraction framework that integrates scientific literature and multi-source spatiotemporal data, aiming to address these limitations by acquiring robust and reliable susceptibility knowledge. First, a landslide susceptibility ontology is designed to systematically organize domain knowledge, encompassing disaster-causing factors, disaster-prone environments, and bearing body attributes. Knowledge extraction employs the improved CasRel model (ERNIE-CasRel) model to derive entity-relationship triples from unstructured literature and then mine knowledge representing expert experience from the triples. Simultaneously, this study integrates self-organizing maps (SOM) and Apriori algorithms to mine spatial aggregation patterns and association rules from structured datasets. The extracted knowledge is then semantically aligned and conflicts are resolved before being integrated into a queryable knowledge graph, which is subsequently stored in Neo4j. Experiments conducted in Yunnan Province, China, validate the efficacy of the proposed framework. Specifically, the ERNIE-CasRel model achieves an F1-score of 0.752 for triple extraction, while the integration of self-organizing maps (SOM) and Apriori algorithms identifies high-confidence association rules. Furthermore, cross-validation leveraging historical landslide data confirms the reliability of these extracted rules. This study advances intelligent landslide susceptibility evaluation (LSE) by synergizing domain knowledge with data-driven techniques, thereby providing a scalable and adaptable solution for geological hazard management. The potential applicability of the proposed methodology to other regions and types of hazards underscores its significant potential for integration into knowledge-based disaster mitigation systems.