可解释的机器学习模型用于基于地理空间变量进行户外超值水平预测
Ciro Régulo Martínez1,2, Débora Pollicelli3, Juan Bajo1,4
1Instituto de Ciencias e Ingeniería de la Computación, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional del Sur, Bahía Blanca, Buenos Aires B8000, Argentina.
The Journal of the Acoustical Society of America
|January 16, 2026
概括
数据驱动的声音水平模型使用地理空间数据预测声学环境. 结合城市数据的模型表现更好,突出了改善户外音景预测的潜力.
科学领域:
- 环境声学环境声学
- 地理空间数据分析.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 声级建模对于理解声环境至关重要.
- 之前的研究集中在使用特定数据集的声音超标水平.
- 不同的环境,从国家公园到城市地区,带来独特的声学挑战.
研究的目的:
- 开发和分析数据驱动的随机森林回归模型,用于预测声音超值水平.
- 使用地理空间变量评估模型性能.
- 评估城市数据对预测准确性的影响.
主要方法:
- 利用来自美国不同地区的声超标水平数据集.
- 应用高级 Python 库来训练随机森林回归模型.
- 整合了99个地理空间变量来预测声音水平.
- 开发了3个通用和5个辅助数据驱动模型.
主要成果:
- 实现了有前途的预测能力,R平方值在0.54到0.91.91之间.
- 根平均平方误差在1.77和5.97dB之间.
- 包含更多城市数据的模型表现出卓越的性能.
- 性能变化与各种环境覆盖的数据集限制有关.
结论:
- 数据驱动型号显示出预测室外声音水平的巨大潜力.
- 城市声学数据集成可以提高模型的准确性.
- 进一步开发需要涵盖更广泛自然和城市环境的数据集.
- 交互式在线仪表板提高了非专家的可访问性.
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