Development of ANFIS models for PM short-term prediction. case study

Abstract

The growing rate of urban and industrial development leads to high levels of air pollution in most countries around the world. Because air pollution has a major impact on human health, monitoring and forecasting of the most important pollutants concentrations are very important. The modelling of the non-linear and complex phenomena associated to air pollution is successfully performed using artificial intelligence-based methods. This paper aims to develop a model based on adaptive neuro-fuzzy inference system (ANFIS) technique for short-term prediction of particulate matter (PM) concentration. There are proposed three models, one that uses only PM concentrations as inputs, and the other two that have as additional inputs meteorological parameters. All models have as output the prediction of the next hour PM concentration. The simulation results for the three proposed models are compared using statistical indices, the best model being the one that takes into account the current hour temperature as additional input besides the PM concentrations.