一种利用元启发式和集体学习的混合方法,用于对污染物度的时间敏感预测
Priya Kansal1, Jatin Bedi2, Sushma Jain2
1Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Bhadson Road, Patiala, Punjab, 147004, India. pkansal_phd22@thapar.edu.
Scientific reports
|November 7, 2025
概括
这项研究引入了一种混合深度学习模型,将CNN,LSTM,爬行动物搜索算法 (RSA) 和XGBoost结合起来,以准确预测空气污染物,最多可提前10天. 这种新的方法显著提高了对城市空气质量的预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 传统的深度学习模型,如CNN和LSTM在准确预测时间序列数据方面存在局限性,特别是在复杂的环境因素方面.
- 准确的空气污染物预测对于公共卫生和城市规划至关重要,特别是在快速发展的城市地区.
研究的目的:
- 开发和评估一种新的混合深度学习模型,集成CNN,LSTM,爬行动物搜索算法 (RSA) 和极端梯度提升 (XGB) 以提高污染物度预测.
- 评估该模型在预测主要空气污染物 (如PM2.5,CO,SO2,NO2) 中的性能,在印度城市环境中提前10天.
主要方法:
- 数据预处理涉及使用Min-Max缩放器进行清洁和正常化.
- 通过整合CNN和LSTM进行特征提取,通过RSA进行优化,由XGB决定特征重要性,构建了一个混合模型.
- 该模型使用来自印度城市环境的污染物度数据进行了训练和验证.
主要成果:
- 与包括变压器,CNN,BiLSTM,BiRNN,ANN和BiGRU在内的基准模型相比,拟的混合模型表现出卓越的准确性和稳定性.
- 该模型在所有测试的污染物中实现了大幅降低预测误差和更高的R平方得分.
- 使用XGB的特征重要性分析提供了对不同特征对预测性能的贡献的见解.
结论:
- 混合CNN-LSTM-RSA-XGB模型为长期空气质量预测提供了可靠和准确的解决方案.
- 这种方法有效地解决了传统模型的局限性,为环境监测和公共卫生管理提供了有价值的工具.
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