基于人工智能支持的深度学习框架,对沙特阿拉伯城市的空气污染进行空间时间预测
Rafat Zrieq1,2, Souad Kamel3, Faris Al-Hamazani4
1Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il 55471, Saudi Arabia.
Toxics
|August 27, 2025
概括
这项研究使用长期短期记忆 (LSTM) 深度学习来模拟沙特阿拉伯的空气污染,其性能优于其他准确预测PM10,PM2.5,CO和O3水平的方法. 通过历史数据来支持环境和健康决策.
科学领域:
- 环境科学
- 数据科学
- 公共卫生
背景情况:
- 工业化和经济活动正在增加全球和沙特阿拉伯的空气污染,
- 对于沙特阿拉伯有效的环境和健康决策而言, 空气污染的数学建模至关重要.
- 现有的空气质量监测网络需要补充的建模方法进行综合评估.
研究的目的:
- 开发和评估沙特阿拉伯的时间和空间空气污染模型的数据驱动深度学习模型.
- 评估长期短期记忆 (LSTM) 算法的性能与随机森林和XGBoost等组合方法相比.
- 研究气象因素对污染物度的影响及其对模型准确性的影响.
主要方法:
- 使用历史污染物记录 (PM10,PM2.5,CO,O3) 和时间序列分析的数据驱动方法.
- 实施深度学习 (DL) 长期短期记忆 (LSTM) 算法用于时间建模.
- 空间建模主要集中在沙特主要城市,并与随机森林 (RF) 和极度梯度增强 (XGBoost) 组合方法进行比较.
主要成果:
- 在预测空气污染物度方面,LSTM表现出卓越的表现,特别是在更大的数据集中,达到高达0.8073的R2值.
- LSTM有效地捕获了数据中的复杂隐藏关系,在预测准确性方面超过了RF和XGBoost.
- 气象因素,包括环境温度,与污染物水平存在微弱至中度的关联,并且它们的纳入并没有显著提高LSTM的预测准确性.
结论:
- 开发的基于LSTM的方法提供了沙特阿拉伯空气污染的准确时间和空间建模.
- 这种数据驱动的方法为支持环境和健康政策决策提供了有价值的工具.
- 该研究强调了深度学习在使用历史时间序列数据的空气质量监测和管理方面的潜力.
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