一种混合的哈里斯·霍克斯优化与支持向量的回归,用于空气质量预测.
Essam H Houssein1, Meran Mohamed2, Eman M G Younis2
1Faculty of Computers and Information, Minia University, Minia, Egypt. essam.halim@mu.edu.eg.
Scientific reports
|January 17, 2025
概括
本研究引入了哈里斯·霍克斯优化支持向量回归 (HHO-SVR) 模型,用于准确的空气质量预测,特别是预测颗粒物质水平. 与现有方法相比,HHO-SVR模型显示出更高的性能.
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:
- 准确的空气质量预测对于公共卫生和环境监测至关重要.
- 现有的预测模型在准确性和效率方面面临挑战.
研究的目的:
- 开发和评估一种新的混合模型,用于预测颗粒物质 ([公式:见文本]) 水平.
- 提高空气质量预测的准确性和可靠性.
主要方法:
- 结合支持向量回归 (SVR) 和哈里斯·霍克斯优化 (HHO) 的混合模型被开发出来 (HHO-SVR).
- 该模型经过训练和测试,使用环境保护局的下调模型 (DS) 的五个数据集进行了测试.
- 使用包括平均绝对百分比误差 (MAPE),平均值,标准偏差 (SD),最佳合适,最不合适和CPU时间在内的指标来评估性能.
主要成果:
- 拟议的HHO-SVR模型显著优于最近公布的模型,如GWO,SSA,HGSO,BMO,WOA和MRFO.
- HHO-SVR模型取得了卓越的结果,成为最佳预测模型.
- 关键性能指标证实了该模型在预测颗粒物质水平方面的有效性.
结论:
- 混合的HHO-SVR模型为空气质量预测提供了一个非常有效的方法.
- 这种新的方法在预测颗粒物度方面取得了重大进展.
- 该研究强调了优化算法与环境应用中的机器学习相结合的优化算法的潜力.
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