基于机器学习的热应答研究,对大学生进行夏季户外军事训练
Bin Yang1,2, Luting Bai1,2, Miao Guo1,2
1School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin, 300384, China.
International journal of biometeorology
|January 29, 2026
概括
视觉感知是预测军事训练期间大学生户外热舒适度的关键. 整合视觉数据显著提高了预测准确度,突出了其在设计舒适的户外空间方面的重要性.
科学领域:
- 环境科学 环境科学
- 人类热舒适度的人类热舒适度
- 心理物理学的精神物理.
背景情况:
- 人类的热舒适度受环境因素和个人感知的影响.
- 视觉感知在热舒适度中的作用,特别是在户外环境中,如军事训练,研究不足.
研究的目的:
- 调查大学生在军事训练期间的户外热舒适度.
- 评估机器学习算法和用于预测热舒适度的功能.
- 评估视觉感知对热舒适度预测的影响.
主要方法:
- 一项问卷调查收集了1754名大学新生的热和视觉感知.
- 持续监测微气候条件.
- 八个机器学习算法被用来开发热舒适度预测模型.
主要成果:
- 结合视觉数据的模型显示出卓越的预测性能.
- 热可接受性模型获得了最高的准确性 (68.2%),比仅使用预测平均投票 (PMV) 的模型增加了34.1%.
- 视觉舒适度投票成为热舒适度最重要的预测因素.
结论:
- 视觉舒适度是预测户外军事训练环境中学生的热舒适度的一个关键因素.
- 机器学习模型,特别是包括视觉数据的模型,可以更好地预测热舒适度.
- 研究结果支持基于证据的户外公共空间设计.
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