基于CNN-BiLSTM模型的空气污染物的多站协作预测
1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China. yananlu_nan@163.com.
概括
本研究引入了一种先进的混合深度学习模型,用于准确预测空气质量,分析天津的气象因素和污染物度. 该模型在预测关键污染物方面表现出高度准确性,有助于环境管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 工业发展带来了严重的空气污染挑战.
- 准确的空气质量预测模型对于实施有效的控制措施至关重要.
- 了解气象因素与污染物度之间的关系至关重要.
研究的目的:
- 使用深度学习开发高精度空气质量预测模型.
- 分析气象因素对中国天津空气污染物度的影响.
- 通过超参数优化和多站式协作方法来增强模型概括性和预测准确性.
主要方法:
- 利用天津4年的空气质量和气象数据.
- 开发了一种混合深度学习模型,结合了卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM).
- 应用贝叶斯优化用于超参数调整,并引入强相关站 (SCS) 概念用于多站协作预测.
主要成果:
- 混合CNN-BiLSTM模型实现了各种污染物的高预测准确性.
- 确定系数 (R2) 为PM2.5,PM10为0.84,SO2为0.69,NO2为0.83,CO为0.92,O3为0.84,PM2.5为0.89,PM10为0.84,SO2为0.69,NO2为0.83,CO为0.92,O3为0.84,而O3则为0.84.
- 使用SCS的多站协作方法进一步提高了预测性能.
结论:
- 拟议的混合深度学习模型有效地预测空气污染物度,准确度令人满意.
- 气象数据的整合,超参数优化和多站协作增强了预测能力.
- 这种方法为城市环境中的空气质量监测和管理提供了一个强大的工具.
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