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住居の耐火性能と防火スペースによる山火事被害の50%削減可能性(California communities can reduce wildfire damage by half. Here’s how.)

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2025-08-28 カリフォルニア大学バークレー校(UCB)

UCバークレー主導の研究は、住宅の「ホーム・ハーデニング(耐火素材の外壁や屋根、火の粉侵入防止の換気口、強化ガラス窓など)」と「防衛空間(家屋周囲1.5mの植生除去)」を組み合わせることで、山火事による被害を最大半減できると示した。過去の大規模火災の被害データや建物条件を用いた解析では、建物の焼失率を約17%減少させ、生存率を倍増させる効果が確認された。研究は防災投資の有効性を裏付け、地域全体での対策普及が被害軽減に不可欠であると指摘している。

住居の耐火性能と防火スペースによる山火事被害の50%削減可能性(California communities can reduce wildfire damage by half. Here’s how.)
Examples of computer simulations of the 2018 Camp Fire and 2019 Kincade Fire. Purple and white shading shows the time of arrival of the fire, and points indicate the locations of structures that were either not damaged, damaged or destroyed. Courtesy of Michael Gollner

<関連情報>

カリフォルニア州の森林都市境界域における構造物への火災リスク Fire risk to structures in California’s Wildland-Urban Interface

Maryam Zamanialaei,Daniel San Martin,Maria Theodori,Dwi Marhaendro Jati Purnomo,Ali Tohidi,Chris Lautenberger,Yiren Qin,Arnaud Trouvé & Michael Gollner
Nature Communications  Published:DOI:https://doi.org/10.1038/s41467-025-63386-2

Abstract

The destructive impacts of wildfires on people, property and the environment have dramatically increased, especially in the Wildland-Urban Interface (WUI) in California. In these areas structures are threatened by both approaching flames and lofted embers which spread fire into and within communities. While independent factors influencing structure fire protection are well known, their combined effects remain largely unquantified, limiting the accuracy of risk assessments and mitigation strategies. Here, we examine five major historical WUI fires—2017 Tubbs, 2017 Thomas, 2018 Camp, 2019 Kincade, and 2020 Glass Fires—utilizing machine learning (ML) analysis of on-the-ground post-fire data collection, remotely sensed data, and fire reconstruction modeling to assess patterns of structure loss and mitigation effectiveness. We show that the spacing between structures is a critical factor influencing fire risk, highlighting the importance of structure arrangement, while fire exposure, the ignition resistance (hardening) of structures, and clearing around structures (defensible space) work in combination to mediate fire risk. Utilizing an XGBoost classifier, structure survivability can be predicted to 82% accuracy. Results highlight the effectiveness of hardening and defensible space, with a hypothetical 52% reduction in losses. Our findings emphasize the need for community-level mitigation to reduce structure loss in future WUI fires.

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