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Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth

Author

Listed:
  • Yasser Ebrahimian Ghajari

    (Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol 47148-71167, Iran)

  • Mehrdad Kaveh

    (ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain)

  • Diego Martín

    (ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain)

Abstract

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R 2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter.

Suggested Citation

  • Yasser Ebrahimian Ghajari & Mehrdad Kaveh & Diego Martín, 2023. "Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth," Mathematics, MDPI, vol. 11(19), pages 1-22, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4145-:d:1252085
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    References listed on IDEAS

    as
    1. Mengjie Wang & Yanjun Wang & Fei Teng & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Estimation and Analysis of PM 2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China," IJERPH, MDPI, vol. 19(7), pages 1-18, April.
    2. Wan Yun Hong & David Koh & Liya E. Yu, 2022. "Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM 2.5 and PM 10 ) Concentrations," IJERPH, MDPI, vol. 19(13), pages 1-32, June.
    3. Kaveh, Mehrdad & Mesgari, Mohammad Saadi & Saeidian, Bahram, 2023. "Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 95-135.
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