School of Environmental Science & Engineering, Tianjin University, Tianjin 300072, China.
Tianjin Research Institute for Water Transport Engineering, M.O.T., Tianjin 300456, China.
Comput Intell Neurosci. 2022 May 31;2022:6393877. doi: 10.1155/2022/6393877. eCollection 2022.
In order to study a BP neural network algorithm for air particulate matter data monitoring, firstly, the monitoring data collected by particle sensor using the light scattering method are proposed. Then, based on the improved BP neural network method, the mapping relationship between the actual measured value of the sensor, weather and other influencing factors, and the standard value of the monitoring station is established, and the calibration model of air particulate matter is realized. Finally, through theoretical analysis and experimental comparison, the results show that the model based on BP neural network algorithm has good accuracy and generalization ability in the evaluation of air particulate index, which makes it possible to scientifically and accurately refine the evaluation and management of urban air particulate index. The experimental results show that the air particle calibration model based on the light scattering method and improved BP neural network algorithm is practical and effective.
为了研究用于空气颗粒物数据监测的 BP 神经网络算法，首先提出了使用光散射法的颗粒物传感器所采集的监测数据。然后，基于改进的 BP 神经网络方法，建立了传感器实际测量值、天气和其他影响因素与监测站标准值之间的映射关系，实现了空气颗粒物的校准模型。最后，通过理论分析和实验比较，结果表明，基于 BP 神经网络算法的模型在空气颗粒物指数评估中具有良好的准确性和泛化能力，这使得科学、准确地细化城市空气颗粒物指数的评估和管理成为可能。实验结果表明，基于光散射法和改进的 BP 神经网络算法的空气粒子校准模型是实用且有效的。
