Potential Impacts of Wildfire on Distribution Networks in Thailand: A Data-Driven Assessment Using Hotspot Analysis

dc.contributor.authorPongpavee Phantanaikasem
dc.contributor.authorAkekachai Pannawan
dc.contributor.authorChaowanan Jamroen
dc.contributor.authorTossaporn Surinkaew
dc.date.accessioned2026-05-08T19:19:40Z
dc.date.issued2024-12-19
dc.description.abstractIn Thailand, the risk of wildfires has increased due to climate change, deforestation, and land-use practices. Distribution networks with feeders that traverse fire-prone forests and grasslands are particularly vulnerable to wildfire damage. Therefore, this study assesses the potential risks that wildfires pose to distribution networks in Thailand by analyzing wildfire hotspot data across different regions. Hotspot data, derived from satellite imagery, provides valuable insights into the frequency and intensity of wildfires in specific areas, which can be correlated with the location and layout of distribution networks. This study aims to identify areas where distribution networks could be at the greatest risk of wildfire-induced disruptions. The study focuses on four major regions of Thailand: the Northern, Northeastern, Central, and Southern regions. Each region has distinct geographical and climatic characteristics that influence wildfire patterns and their potential impacts on infrastructure. By analyzing regional variations in wildfire hotspots, this study aims to provide a preliminary assessment of wildfire risks to distribution networks and offer insights into potential mitigation strategies. The study results are valuable for policymakers, utility companies, and emergency management agencies as they seek to strengthen the resilience of Thailand’s electricity distribution system in the face of increasing wildfire threats.
dc.identifier.doi10.1109/scored64708.2024.10872680
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17132
dc.subjectFire effects on ecosystems
dc.titlePotential Impacts of Wildfire on Distribution Networks in Thailand: A Data-Driven Assessment Using Hotspot Analysis
dc.typeArticle

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