Industrial Environmental Pollution Monitoring and Prediction Analysis Using IoT and Machine Learning

Monitoring and predicting air quality become the most significant operations in many urban and industrial regions. Various types of pollution have a substantial impact on air quality. Effective air quality monitoring models are needed in light of the increased air pollution; these models gather information on air pollutant concentrations. This study describes a comprehensive air quality monitoring architecture built around the (IoT model and fully integrated into existing industrial architectures. It involves the creation of two small, high-precision devices for real-time monitoring of polluting gases and particles in the environment. These gadgets can also capture helpful information such as the humidity and temperature of the area. Machine learning (ML) techniques were also used for the datasets retrieved by the system. The results revealed that the long short-term memory (LSTM) technique was the most accurate in the device's air quality dataset. For example, this gives the proposed solution intelligence to forecast when security standards may be exceeded.