评估机器学习模型和对印度城市空气质量指数预测的归算策略
Salvator Lawrence1, Srimuruganandam Bhathmanabhan2
1Department of Environmental and Water Resources Engineering, School of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Environmental monitoring and assessment
|November 6, 2025
概括
kNNI-MLP模型通过有效处理缺失数据,提供了优异的空气质量指数 (AQI) 预测. 该框架为城市健康风险管理提供了准确的预测.
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:
- 准确的空气质量指数 (AQI) 预测对于城市健康风险管理至关重要.
- 传统方法在缺乏数据和复杂的污染物相互作用方面扎.
研究的目的:
- 通过结合归算技术和机器学习模型来确定AQI预测的最有效框架.
- 评估用于AQI预测的各种归算-ML模型组合的性能.
主要方法:
- 测试了14种归算技术与5种机器学习 (ML) 模型相结合,用于AQI预测.
- 利用了来自印度南部沿海城市奈的六个站的三年的数据.
- 员工统计指标 (R2,RMSE,MAE,SMAPE,MASE) 用于绩效评估.
主要成果:
- 多层感知器 (MLP) 模型与k-近邻归算 (kNNI-MLP) 证明了卓越的性能.
- 获得了0.9999的确定系数和最小误差指标 (RMSE:0.4920,MAE:0.2723).
- 确定了季节性模式和特定地点的污染驱动因素,通过残留和校准分析证实了模型的准确性.
结论:
- kNNI-MLP框架是一个强大的解决方案,用于在城市环境中准确的AQI预测.
- 调查结果支持针对污染控制的有针对性的干预措施,包括交通管制和废物管理.
- 未来的研究将重点关注外部验证和模型可解释性,以获得更广泛的适用性.
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