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空气质量指数预测与优化启用了物联网应用中的深度学习模型.

Sivakumar Sigamani1

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tiruchirappalli, India.

Environmental technology
|October 28, 2024
PubMed
概括

本研究介绍了一种物联网 (IoT) 和深度学习 (DL) 方法,用于准确预测空气质量指数 (AQI). 这种新的方法优化了数据路由,并使用深度前神经网络 (DFNN) 进行精确的空气质量预测.

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:

  • 工业和城市发展导致严重的空气污染,影响人类健康和大气.
  • 精确测量空气质量指数 (AQI) 是至关重要的,并取决于复杂的环境因素,如排放和化学反应.

研究的目的:

  • 开发基于物联网 (IoT) 的深度学习 (DL) 技术来预测空气质量.
  • 提高数据传输的效率和空气质量预测的准确性.

主要方法:

  • 实现了一个物联网模拟与一个新的触点两阶段算法 (TTSA) 有效的数据路由到基站 (BS).
  • 利用Z-score规范化和特征指标提取用于数据预处理.
  • 采用深度前神经网络 (DFNN),通过分数触点双阶段优化 (FTTSA) 进行优化,用于AQI预测.

主要成果:

  • TTSA路由机制以0.979J的能量消耗,0.025s的时间和0.196m的距离实现了卓越的性能.
  • 该DFNN模型在AQI预测中表现出高准确度,RMSE为0.602,R平方为0.598,MSE为0.362,MAPE为0.456.

结论:

  • 拟议的基于物联网的DL技术有效预测空气质量.
关键词:
空气质量指数是指空气质量指数.深度Feedforward神经网络的神经网络分数微积分的计算.触角搜索算法 触角搜索算法两个阶段的优化.

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  • 整合TTSA和FTTSA优化了系统的高效数据处理和准确的AQI预测.