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The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a 'sequence' modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. [ABSTRACT FROM AUTHOR]

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters. [ABSTRACT FROM AUTHOR]