IoT based Intelligent Pollution Monitoring System using Machine Learning Technique

Abstract

In recent rising environmental concerns, the need for efficient pollution monitoring systems has grown critical. This is because traditional methods face substantial hurdles such as restricted spatial analysis, elevated costs, as well as delayed data processing, impeding their effectiveness in addressing the escalating pollution crisis. Moreover, the traditional statistical models may fall short in apprehending the complicated and non-linear relationships inherent in pollution data, thereby regulating their predictive abilities. The Machine Learning (ML) based Artificial Neural Network (ANN) is an efficient approach that is a promising solution for IoT based intelligent pollution monitoring system to address the limitations of traditional pollution monitoring systems. By harnessing the potential of ANN, the proposed approach empowers decision-makers with an intelligent and efficient pollution monitoring system, thereby paving the way for proactive pollution control strategies. This proposed approach has the potential to revolutionize pollution monitoring with its scalable solution, as simulations are demonstrating accuracy 91.3% compared to the previous published approaches.