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2023ZR01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency\u2013Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility.<\/jats:p>","DOI":"10.3390\/rs16132399","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Integrating Knowledge Graph and Machine Learning Methods for Landslide Susceptibility Assessment"],"prefix":"10.3390","volume":"16","author":[{"given":"Qirui","family":"Wu","sequence":"first","affiliation":[{"name":"School of Future Technology, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Future Technology, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Computer and Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinjun","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Computer and Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6838-4309","authenticated-orcid":false,"given":"Jianguo","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liufeng","family":"Tao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Computer and Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.geomorph.2010.12.030","article-title":"Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake","volume":"133","author":"Gorum","year":"2011","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102682","DOI":"10.1016\/j.ijdrr.2021.102682","article-title":"Cross-validation as a step toward the integration of local and scientific knowledge of geologic hazards in rural Peru","volume":"67","author":"Malone","year":"2022","journal-title":"Int. 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