Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Abstract

Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring (OSSO-HDLAPM) in ITS environment. The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants. In addition, hybridized Convolution Neural Network with Long Short-Term Memory (HCNN-LSTM) model is used to predict pollutant level based on the data attained earlier by the sensors. In HCNN-LSTM model, the hyperparameters are selected and optimized using OSSO algorithm. In order to validate the performance of the proposed OSSO-HDLAPM technique, a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

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Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

“…Machine learning (ML), a core AI discipline, employs a spectrum of statistical algorithms that provide computer systems with the ability to autonomously learn from data and progressively enhance their performance in tasks [4,100]. This iterative learning capability without explicit programming has positioned ML as a pivotal tool in air quality monitoring and forecasting, delivering innovative methodologies to analyze extensive datasets, extract complex patterns, and predict future air quality trends [27,101].…”

“…AI-powered systems in air quality monitoring have a variety of possible uses. For instance, they can be used for air quality forecasting [20,21], source identification [22], anomaly detection [23,24], fault diagnosis [25], event detection [26], air pollution control in ITS [27], exposure assessment [28], environmental and health impact assessment [29][30][31], and air quality monitoring network optimization [13]. These applications have the potential to improve the accuracy and efficiency of air quality monitoring and forecasting systems.…”