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Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air

Raj Anand Sundaramoorthy1, Antony Dennis Ananth1, Koteeswaran Seerangan2

  • 1School of Computing, SASTRA (Deemed to be University), Tirumalaisamudram, Thanjavur, Tamil Nadu, 613401, India.

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Summary

A new deep learning model significantly improves air quality prediction by using the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA) and Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN). This advanced system offers more accurate forecasting for ambient air quality.

Keywords:
Air Quality IndexAmbient air quality predictionContamination of airFused Eurasian Oystercatcher-Pathfinder AlgorithmMultiscale depth-wise separable adaptive temporal convolutional network

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Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Rapid industrialization and urbanization in emerging nations have led to severe air pollution, impacting global sustainability and human health.
  • Existing air quality prediction models often use shallow approaches with unsatisfactory accuracy.
  • There is a critical need for advanced, reliable methods for forecasting ambient air quality.

Purpose of the Study:

  • To develop and evaluate a novel deep learning architecture for accurate ambient air quality prediction (AQP).
  • To integrate a dual optimization algorithm for feature selection and weight refinement within the deep learning model.
  • To demonstrate the superior performance of the proposed model compared to traditional methods.

Main Methods:

  • Utilized three public and one real-world dataset for air quality measurements, followed by data consolidation and cleaning.
  • Applied the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA), combining EOO and PFA, for weighted feature selection and optimization.
  • Incorporated optimized features into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for AQP, with further refinement by FEO-PFA.

Main Results:

  • The proposed FEO-PFA optimized MDS-ATCN model demonstrated superior effectiveness over traditional methods.
  • Achieved a reduction in the average cost function by 5.5%.
  • Reduced Mean Absolute Error (MAE) by 28% and Root Mean Square Error (RMSE) by 14% across all datasets.

Conclusions:

  • The developed deep learning model, enhanced by FEO-PFA, provides a significant advancement in ambient air quality prediction.
  • The study highlights the potential of sophisticated deep learning architectures and hybrid optimization algorithms for environmental monitoring.
  • The findings offer a promising solution for more accurate and reliable air quality forecasting, crucial for public health and sustainability efforts.