Enabling Air Pollution Prediction through IoT and Machine Learning

Large scale industrialization and the increase in the number of factories and industries across major cities in the world have been contributing to the decreasing air quality. This is since a rapid increase in the population across the world has prompted the majority of the companies across the globe to adopt mass production activities to keep up with the increasing demand. This is evident in the fact that most of the big cities have an increasing number of cases of respiratory illnesses and asthmatic symptoms in the populous. Therefore, there is an urgent need to address these issues to provide a better environment and reduce such incidences. The Internet of Things or IoT platform is a quite a promising platform for this approach which has been getting increasingly affordable and approachable. Therefore, in the approach stipulated in this research, the IoT platform has been utilized in addition to the Machine Learning paradigms to achieve accurate air quality predictions. The proposed methodology utilizes K nearest neighbors and Linear Regression, along with the Hidden Markov Model for effective Pollution level estimation. Suraj Kapse | Akshay Kurumkar | Vighnesh Manthapurwar | Prof. Rajesh Tak "Enabling Air Pollution Prediction through IoT and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30739.pdf