与ARIMA和ANN整合量子回归,以便在泰国哈特 (Hat Yai) 进行可解释和准确的PM2.5预测
Jularat Chumnaul1, Kasikrit Damkliang2
1Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand. jularat.c@psu.ac.th.
Scientific reports
|December 8, 2025
概括
准确的PM2.5预测对公共卫生至关重要. 结合ARIMA,ANN和量子位回归 (ARIMA-ANN-QREG) 的新混合模型显著提高了预测准确性,为空气质量管理提供了强大的工具.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 准确预测PM2.5度对于空气质量管理和公共卫生保护至关重要.
- 现有的模型可能在捕捉复杂的时间依赖和极端事件方面存在局限性.
研究的目的:
- 开发和评估一种新型混合模型,集成自回归集成移动平均线 (ARIMA),人工神经网络 (ANN) 和定量回归,用于增强PM2.5预测.
- 评估模型的性能与使用现实数据的基线和独立模型相比.
主要方法:
- 一种混合ARIMA-ANN-QREG模型被开发并使用来自泰国Hat Yai (2020-2024) 的每日PM2.5数据进行测试.
- 数据预处理包括处理缺失值,然后将数据分成训练和测试集.
- 模型的性能被使用诸如平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和平均分数偏差 (MFB) 等指标来评估.
主要成果:
- 与独立的ARIMA或ANN模型相比,混合ARIMA-ANN模型表现出优异的预测性能.
- 拟议的ARIMA-ANN-QREG模型实现了最低的MAE (1.704),MAPE (11.782%) 和最小偏差 (MFB = -0.0004).
- 该模型即使在极端的PM2.5度条件下也被证明是有效的.
结论:
- ARIMA-ANN-QREG模型为PM2.5预测提供了一个强大的,计算效率高的,可解释的替代方案.
- 这种方法为现实世界空气质量管理提供了一个实用的工具,支持政策制定和预警系统.
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