IJGI, Free Full-Text
Por um escritor misterioso
Last updated 03 julho 2024
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Typhoons are destructive weather events. News media reports contain large amounts of typhoon information. Transforming this information into useful knowledge to provide a basis for mining typhoon knowledge and supporting disaster prevention and relief is urgently required to solve this problem. Knowledge representation can be used to address this problem, although it presents several challenges. These challenges lie in expressing the static and dynamic characteristics of typhoons and formalizing the knowledge representation method and making it suitable for machine processing. Moreover, the general Chinese time and space representation method is overly cumbersome for use in ontologies. The present study proposes an ontology-based typhoon event representation method that solves the representation problems of the typhoon static concept and dynamic features. Furthermore, it summarizes the fixed patterns of time and space in Chinese news and designs a time and space model suitable for typhoon event ontologies. From the ontology population, typhoon event ontology instances are created, and the typhoon event ontology model is applied to the analysis of typhoon processes, verifying the effectiveness of the typhoon event ontology model.
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IJGI, Free Full-Text
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Marmot Dataset Get File - Colaboratory
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Serial Number 3D World Map 2.1 - Colaboratory
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How to get better search results for tech discovery - Mergeflow
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IJGI, Free Full-Text
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Frontiers Monitoring Changes and Soil Characterization in Mangrove Forests of the United Arab Emirates Using the Canonical Correlation Forest Model by Multitemporal of Landsat Data
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Full article: Decision tree based ensemble machine learning approaches for landslide susceptibility mapping
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Challenges and opportunities for large-scale reforestation in the Eastern using native species - ScienceDirect
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Driver And Vehicle Computer Services Division Shannon - Colaboratory
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IJGI, Free Full-Text
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Full article: Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
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