ACBiGRU-DAO:注意卷积双向门式循环单元式动态算术优化用于空气质量预测
Vinoth Panneerselvam1, Revathi Thiagarajan2
1Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India. vinoth.ttk@gmail.com.
概括
这项研究引入了一种新的空气质量预测模型,即基于动态算术优化 (ACBiGRU-DAO) 的注意力卷积双向门式循环单元 (Attention Convolutional Bidirectional Gated Recurrent Unit),达到95.34%的准确性. 该模型有效地预测空气质量,并对污染水平进行分类,以改善环境和健康管理.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 空气污染是一个重大的环境和健康问题,特别是在发展中国家.
- 准确的空气质量评估对于城市和工业监测至关重要.
- 现有的预测模型需要改进以提高准确性.
研究的目的:
- 提出一种新的空气质量预测模型,ACBiGRU-DAO.
- 为了提高预测准确度,使用注意力卷积双向门循环单位 (ACBiGRU) 和动态算术优化 (DAO).
- 将空气质量分为六个严重程度阶段.
主要方法:
- 利用印度空气质量数据,包括AQI,PM2.5,PM10,CO,NO2,SO2和O3.
- 通过缺失值赋值和数据转换预处理数据.
- 开发并优化了ACBiGRU-DAO模型用于预测和分类.
主要成果:
- 在ACBiGRU-DAO方法取得了95.34%的准确性.
- 与其他评估方法相比,证明了卓越的性能.
- 成功预测空气质量并将其分为六个AQI阶段.
结论:
- 拟议的ACBiGRU-DAO模型为空气质量预测提供了一个高度准确的解决方案.
- 这种方法可以帮助监测和减轻空气污染的影响.
- 有效的空气质量预测对于公共卫生和环境保护至关重要.
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