PM2.5 prediction method using back propagation neural network

Shedrack Nkom Nuhu; Zhu Duan; Yanfei Li

doi:10.1117/12.2673730

22 May 2023 PM_2.5 prediction method using back propagation neural network

Shedrack Nkom Nuhu, Zhu Duan, Yanfei Li

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401T (2023) https://doi.org/10.1117/12.2673730
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

The effects of air pollution, amongst the greatest problems facing our planet, are felt throughout all spheres of society, including the transportation sector. Its impacts can range from increased risk of illness to rising temperatures. One of the essential situations for improving inner-city general health and assisting in the creation of a sustainable environment is the ability to forecast air pollution concentrations with accuracy and effectiveness. The backpropagation model is employed in this research to predict the future concentration levels of PM2.5. Data on air quality are gathered and used in the experiment. Empirical Wavelet Transform (EWT) is used to break down the air quality data, which is then utilized to train and evaluate the model. 30% of the data collected is utilized during testing, and 70% was used to train the BP model. The evaluation criteria are applied after testing to determine the model's correctness. The Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE) are the evaluation criteria used and their values were 0.0896, 0.8112, and 0.1162, respectively.

Citation Download Citation

Shedrack Nkom Nuhu, Zhu Duan, and Yanfei Li "PM_2.5 prediction method using back propagation neural network", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401T (22 May 2023); https://doi.org/10.1117/12.2673730

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KEYWORDS

Data modeling

Air contamination

Air quality

Education and training

Neural networks

Performance modeling

Artificial neural networks

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