在新德里使用混合极端学习机器与蛇优化算法相结合,改进PM2.5预测.
Adil Masood1, Mohammed Majeed Hameed2, Aman Srivastava3
1Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India.
Scientific reports
|November 29, 2023
概括
本研究介绍了ELM-SO模型,用于预测细颗粒物 (PM2.5) 度. 新的混合方法与其他机器学习模型相比显示出更高的准确性,为空气质量预测提供了有价值的工具.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 微细颗粒物 (PM2.5) 对健康构成重大风险,并在德里等城市地区造成过早死亡.
- 准确的PM2.5度预测对于公共卫生咨询和意识至关重要.
研究的目的:
- 开发和评估一种新的混合模型,即带有蛇优化的极端学习机器 (ELM-SO),用于PM2.5度预测.
- 将ELM-SO模型的预测性能与已建立的机器学习和深度学习模型进行比较.
主要方法:
- 使用空气质量和气象数据开发了一种混合ELM-SO模型.
- 该ELM-SO模型的性能与支持向量回归 (SVR),随机森林 (RF),极端学习机器 (ELM),梯度增强回归器 (GBR),XGBoost和长短期记忆 (LSTM) 网络进行了基准测试.
主要成果:
- 该ELM-SO模型实现了最高的预测准确度.
- ELM-SO模型显示测试R平方值为0.928和根平均平方误差为30.325μg/m3.
结论:
- ELM-SO技术是准确预测PM2.5的高效工具.
- 这种先进的预测能力可以提高对空气污染对健康和环境的影响的理解和预测.
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