对线性回归,神经网络和随机森林回归进行比较分析,用于利用软传感器模型预测空气臭氧
Zheng Zhou1, Cheng Qiu2, Yufan Zhang1
1Department of Material and Environmental Engineering, Chengdu Technological University, Chengdu, China.
Scientific reports
|December 16, 2023
概括
神经网络模型,特别是循环神经网络 (RNN),擅长预测空气臭氧水平. 变量组E和传感器NNR[Y]C显示出最佳性能,为空气质量监测提供可靠的见解.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 准确的空气臭氧预测对于环境监测和公共卫生至关重要.
- 软传感器建模提供了一种具有成本效益的方法来估计臭氧度.
研究的目的:
- 为了比较用于空气臭氧预测的不同软传感器建模技术的性能.
- 评估各种输入变量集对预测准确性的影响.
- 确定最优的建模方法和可靠臭氧预测的变量集.
主要方法:
- 对线性回归 (LR),神经网络 (NN) 和随机森林回归 (RFR) 的比较分析.
- 使用不同的输入变量组合评估预测性能.
- 专注于NN类中的反复神经网络 (RNN).
- 对传感器NNR[Y]C性能指标的详细评估 (R2,RMSE,MAE).
主要成果:
- 神经网络模型,特别是RNN,与LR和RFR相比,显示出更高的预测准确性.
- 变量集E在评估的软传感器模型中实现了最高的平均预测准确性.
- 传感器NNR[Y]C表现出卓越的性能,其R2值为0.8902,RMSE值为24.91和MAE值为19.16.
- 包括PM10并没有提高性能,可能是由于PM2.5的多线性.
结论:
- 循环神经网络对于软传感器对空气臭氧的建模非常有效.
- 变量集E和传感器NNR[Y]C代表了一个强大的组合,用于准确的臭氧预测.
- 这些发现为开发先进的空气质量监测系统提供了宝贵的见解.
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