April 12, 2024

Smart Analysis of Air Pollutants In Urban Areas

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SJSushma S JagtapRajalakshmi Engineering College GIG IndhumathiRajamangala University of Technology Isan RBR BharathyRajalakshmi Engineering College

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Implication

Comparative evaluation reveals the efficacy of deep learning over statistical models for air pollutant forecasting in urban areas, highlighting potential for real-time management.

Key Points

Deep learning architectures forecast urban air pollutants such as NO2, O3, and PM 2.5, addressing complex spatial and temporal dynamics in city environments.
Comparative assessment benchmarked convolutional neural networks and recurrent neural networks directly against linear regression and ARIMA statistical baselines.
High predictive accuracy of deep learning supports real-time monitoring of air pollutants, enabling informed decision-making for urban public health initiatives.

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Jagtap et al. (2024) studied this question.

synapsesocial.com/papers/68e6f722b6db643587671f71 https://doi.org/10.1109/iccsp60870.2024.10544285

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Implication

Comparative evaluation reveals the efficacy of deep learning over statistical models for air pollutant forecasting in urban areas, highlighting potential for real-time management.

Smart Analysis of Air Pollutants In Urban Areas

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Key Points

Cite This Study

Smart Analysis of Air Pollutants In Urban Areas

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Member takes

Implication

Key Points

Cite This Study

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