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Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data.

Authors :
Ge, Xingtong
Yang, Yi
Peng, Ling
Chen, Luanjie
Li, Weichao
Zhang, Wenyue
Chen, Jiahui
Source :
Remote Sensing; Jul2022, Vol. 14 Issue 14, pN.PAG-N.PAG, 21p
Publication Year :
2022

Abstract

Forest fires have frequently occurred and caused great harm to people's lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
14
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
158297777
Full Text :
https://doi.org/10.3390/rs14143496