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使用XRSTH-LSTM算法进行空气污染预测系统.

Harshit Srivastava1, Santos Kumar Das2

  • 1Department of Electronics and Communication, National Institute of Technology, Rourkela, 769008, Odisha, India.

Environmental science and pollution research international
|July 22, 2023
PubMed
概括

这项研究引入了一种新的AI模型,用于使用基于Xavier Reptile Switan-h的长期短期记忆 (XRSTH-LSTM) 预测空气污染 (AP). XRSTH-LSTM模型在预测空气质量方面实现了高准确性,比现有方法有了显著的改进.

关键词:
空气污染预测, AQI, 深度学习, 严重性分析, XRSTH-LSTM

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 全球空气污染 (AP) 的增加对人类健康和生态系统构成重大威胁.
  • 准确预测AP对于开发有效的缓解策略至关重要.
  • 现有的模型经常面临计算成本和精度的挑战.

研究的目的:

  • 开发一种基于人工智能 (AI) 的空气污染预测系统.
  • 提高空气质量指数 (AQI) 预测的准确性和精度.
  • 为了降低与AP预测模型相关的计算成本.

主要方法:

  • 基于Xavier Reptile Switan-h的长期短期记忆 (XRSTH-LSTM) 模型的开发.
  • 通过预处理,属性提取和AQI预测,对XRSTH-LSTM模型进行微调.
  • 利用Kaggle的印度空气质量数据 (2015-2020) 数据集进行培训和验证.
  • 使用诸如MSE,MAPE,RMSE,精度,回忆,F-测量,负预测值和马修相关系数等指标评估模型性能.

主要成果:

  • 拟议的XRSTH-LSTM模型实现了98.52%的精度和99.79%的精度.
  • 与现有的最先进模型相比,这意味着准确度提高了0.74%.
  • 该模型展示了强大的性能和空气质量数据的高效处理.

结论:

  • 新型XRSTH-LSTM模型为空气污染预测提供了高度准确和精确的解决方案.
  • 基于人工智能的方法有效地解决了对高级AP预测的需求.
  • 开发的系统在预测空气污染水平方面表现优于目前的模型.