Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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.