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Wildfires are becoming more frequent due to extreme weather and rising global temperatures, exacerbating climate change by releasing greenhouse gases. To mitigate wildfire risks, this study proposes an advanced early warning system that integrates a complex fuzzy inference system with spatio-temporal satellite imagery and regional temperature data. By leveraging datasets from the U.S. Navy and the National Environmental Information Center, the model effectively predicts wildfire risk levels. Results demonstrate superior accuracy compared to existing methods, highlighting the potential of integrating temperature data with complex fuzzy inference for wildfire forecasting.

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Advancing Wildfire Warning with Spatio-Temporal Fuzzy Inference and Integrated Temperature Data

  • 会议论文

  • 发表日期：

    2026年

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  • Le Truong Giang,
  • Nguyen Van Thien,
  • Le Minh Hoang,
  • Nguyen Van Luong,
  • Nguyen Thi Hong Hanh,
  • Truong Van Khai,
  • Nguyen Long Giang

摘要

Wildfires are becoming more frequent due to extreme weather and rising global temperatures, exacerbating climate change by releasing greenhouse gases. To mitigate wildfire risks, this study proposes an advanced early warning system that integrates a complex fuzzy inference system with spatio-temporal satellite imagery and regional temperature data. By leveraging datasets from the U.S. Navy and the National Environmental Information Center, the model effectively predicts wildfire risk levels. Results demonstrate superior accuracy compared to existing methods, highlighting the potential of integrating temperature data with complex fuzzy inference for wildfire forecasting.