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库里蒂巴的颗粒物预测和预测使用机器学习.
Marianna Gonçalves Dias Chaves1, Adriel Bilharva da Silva2, Emílio Graciliano Ferreira Mercuri3
1Graduate Program of Environmental Engineering, Federal University of Paraná, Curitiba, Brazil.
机器学习模型准确地预测和预测来自车辆排放的颗粒物 (PM2.5) 污染. 这些模型通过了解污染物分散来改善城市地区的空气质量管理.
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:
- 车辆排放是城市空气污染的主要来源,特别是颗粒物 (PM2.5).
- 了解排放,气象和PM2.5之间的相互作用对于公共卫生和政策至关重要.
- 现有的PM2.5预测和预测方法需要改进.
研究的目的:
- 分析车辆排放,气象变量和PM2.5度之间的关系.
- 开发和评估用于预测和预测PM2.5.5.的机器学习模型.
- 为减轻城市环境中车辆排放影响提供见解.
主要方法:
- 利用来自巴西库里蒂巴 (Curitiba) 的气象条件,车辆流量和PM2.5度的每小时和每天数据 (2020-2022).
- 使用随机森林 (RF) 和长期短期记忆 (LSTM) 神经网络进行预测和预测.
- 使用多重线性回归 (MLR) 和天真估计作为基线模型.
主要成果:
- 随机森林实现了高预测准确度 (每天99.37%),确定行星边界层高度作为关键因素.
- 长期短期记忆表现出卓越的预测准确性 (99.71%为1小时的时间).
- 两种RF和LSTM模型都超过了基线MLR和天真方法.
结论:
- 机器学习模型显著提高PM2.5预测和预测的准确性.
- 这项研究为了解污染物散布提供了基础,并为城市空气质量政策提供了信息.
- 准确的预测可以帮助制定减轻车辆排放对健康的影响的策略.
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