IJGI, Free Full-Text

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.

World Tiling/Grid - Theory - Thrive Development Forum

IJGI, Free Full-Text, Lattice Quad-Tree Indexing Algorithm for a Hexagonal Discrete Global Grid System, HTML

Theni Map Free Get File - Colaboratory

Pfeil 4 Temporada Online Herunterladen - Colaboratory

IJGI, Free Full-Text

Full article: Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential

IJGI Free Full-Text Enhanced Drone Navigation In GNSS Denied Environment Using VDM And Hall Effect Sensor

JGI IMG Integrated Microbial Genomes & Microbiomes

IJGI, Free Full-Text

Osm Map Download Germany - Colaboratory

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