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  • 标题:Comparative study on machine learning algorithms for early fire forest detection system using geodata.
  • 本地全文:下载
  • 作者:Zouiten Mohammed ; Chaaouan Hanae ; Setti Larbi
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2020
  • 卷号:10
  • 期号:5
  • 页码:5507-5513
  • DOI:10.11591/ijece.v10i5.pp5507-5513
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要:Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.
  • 关键词:Fire forest detection;Machine learning;Voronoi;Support vector machine;Random forest
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