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模拟颗粒物质 (PM₁₀) 在跨境雾事件期间的变化,使用修改的量子回归方法

Nur Alis Addiena A Rahim^1,2, Norazian Mohamed Noor^1,2, Izzati Amani Mohd Jafri^1,2

¹Faculty of Civil Engineering Technology Universiti Malaysia Perlis Arau Perlis Malaysia.

Analytical science advances

|July 17, 2025

概括

这项研究引入了新的空气质量模型,用于在东南亚雾事件期间预测颗粒物 (PM10) 污染的前 1-3 天. 先进的特征选择提高了模型的准确性,帮助预警系统.

科学领域:

环境科学环境科学
大气化学大气化学
数据科学数据科学数据科学

背景情况:

东南亚的跨境雾造成了严重的环境和健康问题.
准确预测颗粒物 (PM10) 对于管理雾事件至关重要.

研究的目的:

开发和评估马来西亚跨境雾期间PM10度的新型空气质量预测模型.
通过将先进的特征选择技术与量子回归集成来提高预测准确度.

主要方法:

混合模型的开发:量子回归与基于缓解的排名 (QR-Relief),基于关联的选择 (QR-关联) 和主要组件分析 (QR-PCA).
使用诸如平均绝对误差 (MAE),规范绝对误差 (NAE) 和根平均平方误差 (RMSE) 等指标评估模型性能.
从2019年开始使用独立数据集进行验证,以确认现实世界的适用性.

主要成果:

混合模型 (QR-Relief,QR-correlation,QR-PCA) 的表现明显优于传统的量子回归和多重线性回归模型.
特征选择技术明显提高了空气质量模型的预测可靠性.
经过验证的模型表现出强的性能,证实了它们对现实世界的雾事件预测的实用性.

结论:

综合量子回归的高级特征选择为准确的PM10预测提供了强大的框架.
开发的模型为雾事件期间的环境和公共卫生管理提供了有价值的决策支持工具.
这种方法上的进步支持建立有效的跨境雾预警系统.

关键词:

混合建模技术是混合建模技术.颗粒物质颗粒物质颗粒物质预测分析预测分析定量回归的定量回归方法跨境的雾是什么意思

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