Artificial Flora Optimization with Deep Learning Enabled Air Pollution Monitoring System in IoT Environment
Keywords:
Air pollution, Deep learning, Artificial Flora Algorithm, Data acquisition, PredictionAbstract
Air pollution monitoring is ever-growing, which provides increasing priority toward the consequence on human health. A higher level of pollution in air might have a lot of adverse effects on the health of fellow human beings. It increases the possibility of lung cancer, chronic disease, and cardiac disease. Meanwhile, pollution in the air impacts the health and leads to increased mortality and morbidity raised the toxicological analysis focuses on components influencing air pollution. Thus, there is a need for designing an automatic environment pollution monitoring system. With this background this research paper focuses on building an automated pollution monitoring system using Artificial Intelligence techniques. The aim is to construct a deep learning based prediction model that could forecast the level of air pollutants (PM2.5 concentration) with reference to the weather and climatic parameters. The deep learning model was trained using the dataset collected from the benchmarked and real time data. To overcome the problems and pitfalls in the earlier statistical and deep learning based approaches; this research employs Artificial Flora Optimization algorithm for optimization of the deep learning model’s hyperparameters termed as AFODL. The experimental evaluation analyses the performance of the AFODL model under different experimental scenario. The simulation results reported the enhancements of the AFODL based air pollutant forecast model when compared to earlier approaches.
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