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Updated: Sep 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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埋立地漏れリスクの不確実性分析のためのアクティブ・ラーニング強化深層ニューラルネットワーク (AL-DNN)

Huimin Zhang1, Feng Chen2, Ya Xu3

  • 1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Shandong Technology and Business University, Yantai, Shandong 264005, China.

Journal of hazardous materials
|August 31, 2025
PubMed
まとめ

この研究は,埋立地漏水による地下水の汚染リスクの効率的な評価のために,アクティブ・ラーニング強化のディープ・ニューラル・ネットワーク (AL-DNN) モデルを導入します. AL-DNNモデルは,計算時間を大幅に短縮し,正確性を維持し,環境リスク管理を迅速に可能にします.

キーワード:
アクティブ・ラーニング強化ディープニューラルネットワーク地下水リスク評価埋立地漏れのリスク不確実性の分析

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