机器学习和深度学习技术的应用,用于模拟德黑兰大都市城市地区空气污染和气象参数之间的关联
Parisa Kahrari1, Shahriar Khaledi1, Ghasem Keikhosravi2
1Department of Natural Geography, Faculty of Earth Sciences, University of Shahid Beheshti, Tehran, Iran.
Environmental monitoring and assessment
|October 1, 2024
概括
德黑兰 德黑兰 德黑兰
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 德黑兰面临严重的空气污染,特别是在寒冷的月份.
- 了解污染物-气象学联系对于减缓至关重要.
研究的目的:
- 分析德黑兰空气污染物和气象因素之间的统计关系.
- 为了比较六个机器学习算法的空气污染模型的性能.
主要方法:
- 对于标准空气污染物 (CO,NO2,SO2,O3,PM10,PM2.5) 和气象元素,使用了2001-2021年的数据.
- 使用R软件应用并比较了六种算法:MLR,GAM,CART,RF,GBM和DL.
- 基于预测准确性和节性评估模型性能.
主要成果:
- 臭氧 (O3) 与天气条件有很强的相关性.
- 其他污染物 (CO,NO2,SO2,PM10,PM2.5) 的相互影响比气象因素更大.
- 多重线性回归 (MLR) 和通用添加模型 (GAM) 是有效的,在许多情况下,MLR的表现优于.
结论:
- 天气显著影响德黑兰的臭氧水平.
- 需要进一步的研究来确定人类活动对污染物水平的影响.
- MLR和GAM是分析德黑兰空气污染动态的合适模型.
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