一个先进的深度学习预测模型用于空气质量指数预测,使用远程卫星衍生的水气候变量
Abul Abrar Masrur Ahmed1, S Janifer Jabin Jui2, Ekta Sharma2
1Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia.
The Science of the total environment
|September 22, 2023
概括
准确的空气质量指数 (AQI) 预测对发展中国家至关重要. 一个新的混合深度学习模型,CLSTM-BiGRU,使用遥感数据有效预测AQI,优于现有方法.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 空气污染对环境和公共健康构成重大威胁,特别是在发展中国家.
- 对空气质量指数 (AQI) 的准确预测对于有效的环境管理和减轻健康风险至关重要.
研究的目的:
- 引入和评估一种新的混合深度学习模型,用于准确地预测亚热带地区的AQI.
- 评估模型的性能与使用现实数据的基准方法对比.
主要方法:
- 开发一种混合深度学习模型 (CLSTM-BiGRU),集成卷积神经网络 (CNN),长期短期记忆 (LSTM) 和双向封闭反复单元 (BiGRU).
- 利用19个遥感预测变量.
- 使用灰狼优化 (GWO) 算法对模型进行优化.
主要成果:
- 与基准模型相比,CLSTM-BiGRU模型在AQI预测方面表现优越.
- 该模型实现了较低的预测误差和高的相关系数 (约为1).
结论:
- 混合CLSTM-BiGRU模型为准确的AQI预测提供了一个可行的和强大的解决方案.
- 该模型在城市地区对空气质量监测和公共卫生风险减轻具有重大潜力.
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