基于CEEMDAN分解方法与SVM和LSTM集成的PM 2.5度预测
Rasoul Ameri1, Chung-Chian Hsu2, Shahab S Band3
1Department of Information Management, National Yunlin University of Science and Technology, Douliou, Taiwan.
Ecotoxicology and environmental safety
|October 14, 2023
概括
准确预测颗粒物 (PM 2.5) 对可持续发展至关重要. 本研究引入了一种新的CEEMDAN-SVM-LSTM模型,用于优质的PM 2.5预测,其性能优于现有的短期预测方法.
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:
- 城市化和消费增加了空气污染,特别是颗粒物 (PM 2.5).
- 准确的PM2.5预测对于减轻健康风险和促进可持续发展至关重要.
- 现有的预测方法难以捕捉PM 2.5度的复杂动态.
研究的目的:
- 开发和评估一种用于准确预测PM 2.5度的新型混合模型.
- 将完整集体实证模式分解与自适应噪声 (CEEMDAN),支持矢量机 (SVM) 和长短期内存 (LSTM) 结合起来,以提高预测.
- 通过使用各种统计指标来评估模型与传统方法的性能.
主要方法:
- 使用CEEMDAN将PM 2.5时间序列分解为内在模式函数 (IMF).
- 应用SVM和LSTM回归模型来预测IMF的各个组成部分.
- 采用天真进化算法来优化模型参数和结合预测.
- 通过台湾高雄 (2019-2021) 的每日PM 2.5数据来训练和验证该模型.
主要成果:
- 拟议的CEEMDAN-SVM-LSTM模型在1天前的PM 2.5预测中表现出卓越的性能.
- 实现了1.858的低平均绝对误差 (MAE),7.2449的平均平方误差 (MSE) 和2.6682的根平均平方误差 (RMSE).
- 达到0.9169的高确定系数 (R2),表明出色的模型合适.
- 该模型还显示了对3天和7天前的PM2.5预测的最佳表现.
结论:
- 混合CEEMDAN-SVM-LSTM方法为PM 2.5预测提供了一个强大而准确的方法.
- 这种先进的预测能力可以支持环境监测和可持续城市规划.
- 该模型在预测长期PM 2.5趋势方面的有效性需要进一步研究.
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