通过基于波量和优化模型的德里高效的多站空气质量预测.
Lakshmi Sankar1, Krishnamoorthy Arasu1
1School of Computer Science Engineering, Vellore Institute of Technology, Vellore, India.
PloS one
|August 19, 2025
概括
在德里,准确的空气质量预测至关重要. 通过使用先进的特征选择和双向长期短期记忆网络,AquaWave-BiLSTM模型增强了预测,改善了卫生政策的制定.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 计算科学 计算科学
背景情况:
- 南亚大城市,特别是德里,面临严重的空气质量退化.
- 高度的颗粒物 (PM2.5) 度对公众健康构成重大风险.
- 有效的空气质量预测对于缓解策略和政策制定至关重要.
研究的目的:
- 引入一个创新的预测框架,AquaWave-BiLSTM,用于德里多站空气质量预测.
- 提高空气质量预测模型的准确性和效率.
- 为预测提供有关特征贡献的可解释的见解.
主要方法:
- 集成波形变换用于频率模式提取.
- 主要组件分析 (PCA) 的应用,以减少维度.
- 混合Aquila优化器和算术优化 (AOAOA) 用于功能选择.
- 使用双向长期短期记忆 (Bi-LSTM) 网络进行时间分析.
- 使用威尔科克森签名等级测试进行统计验证,并通过SHAP进行特征贡献分析.
主要成果:
- 在AquaWave-BiLSTM框架实现了高的预测准确度 (MSE: 0.00065,MAE: 0.04566,RMSE: 0.02523,R2: 0.9494).
- 该模型展示了计算效率,平均执行时间为20.57秒.
- 在所有监测站上统计验证了特征选择和提取方法.
结论:
- AquaWave-BiLSTM为多站空气质量预测提供了一种高效和可解释的解决方案.
- 该框架在预测准确性方面超越了传统方法.
- 该方法为影响空气质量的因素提供了有价值的见解,有助于决策.
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