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Updated: Sep 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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积极学习增强的深度神经网络 (AL-DNN) 用于垃圾填埋场泄漏风险的不确定性分析

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
概括

这项研究引入了一种主动学习增强的深度神经网络 (AL-DNN) 模型,用于有效地评估垃圾填埋场漏的地下水污染风险. AL-DNN模型显著减少了计算时间,同时保持了准确性,使环境风险管理更快.

关键词:
积极学习增强深度神经网络地下水风险评估垃圾填埋场泄漏的风险不确定性分析

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