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使用可解释的人工智能技术进行交通噪音水平分析的自动化框架.

Rohit Patel1, Shashi Kant Tiwari2, Deepak Kumar Rakesh3,4

  • 1Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.

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|October 21, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了可解释的AI (XAI),用于使用机器学习模型预测交通噪音. 随机森林模型,XAI增强,确定两轮车的数量是城市噪音污染的关键因素.

关键词:
可解释的人工智能在 KNN KNN 标签上.随机的森林随机的森林交通噪音水平的交通噪音水平.在XGBoost中使用.

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科学领域:

  • 环境科学 环境科学
  • 人工智能的人工智能
  • 城市规划 城市规划

背景情况:

  • 交通噪音是主要的城市污染物,目前的预测方法往往缺乏可解释性.
  • 先进的机器学习 (ML) 模型为更准确的交通噪音预测提供了潜力.

研究的目的:

  • 应用可解释AI (XAI) 来对交通噪声水平进行回归分析.
  • 为了比较ML模型的性能,包括KNN,XGBoost,LSTM和RF用于交通噪声预测.
  • 确定城市环境中交通噪声的关键预测因素.

主要方法:

  • 利用了丹巴德市的综合交通数据集,包括车辆的速度和类型.
  • 实施并比较了四种ML模型:K-最近邻居 (KNN),极端梯度增强 (XGBoost),长短期记忆 (LSTM) 和随机森林 (RF).
  • 开发了一个基于性能最好的射频回归器的XAI模型.

主要成果:

  • 随机森林 (RF) 模型实现了最高的性能,RMSE为1.27,R-平方为0.94.
  • 基于射频的XAI模型为噪声预测提供了可解释的见解.
  • 两轮车的数量被确定为交通噪音水平的重要预测指标.

结论:

  • 可解释的人工智能增强了用于交通噪音预测的ML模型的解释性.
  • 这些发现可以支持城市规划者制定减轻噪音污染的策略.
  • 该研究强调了ML和XAI在理解和管理城市环境因素方面的有效性.