A machine learning based PM2.5 forecasting framework using internet of environmental things

Information and communication technologies have been widely used to achieve the objective of smart city development. A smart air quality sensing and forecasting system is an important part of a smart city. In this paper, we present an approach to accurately forecast hourly fine particulate matter (PM2.5). An Internet of Things (IoT) framework comprising of Airbox Devices for PM2.5 monitoring has been used to acquire the data. Our main focus is to achieve high forecasting accuracy with reduced computation time. We use a hybrid model to do the forecast and a grid based system to cluster the monitoring stations based on the geographical distance. The experimentation and evaluation is done using Airbox Devices data from 119 stations in Taichung area of Taiwan. We are able to demonstrate that a proper clustering based on geographical distance can reduce the forecasting error rate and also the computation time.