[根据可解释的机器学习揭示城市环境的驱动因素]
Jia-Qi Dong1, Dong-Mei Hu1, Yu-Long Yan2
1Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
Huan jing ke xue= Huanjing kexue
|July 12, 2023
概括
本研究引入了一个可解释的AI模型 (相关性-ML-SHAP),以确定夏季臭氧 (O3) 污染的关键驱动因素. 该模型准确量化了温度和排放等因素对O3水平的影响,有助于城市空气质量管理.
科学领域:
- 大气化学和物理大气化学和物理
- 人工智能和机器学习
- 环境科学与工程环境科学与工程
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