Modeling air pollution by integrating ANFIS and metaheuristic algorithms

Air pollution is increasing for many reasons, such as the crowding of cities, the failure of planning to consider the benefit of society and nature, and the non-implementation of environmental legislation. In the recent era, the impacts of air pollution on human health and the ecosystem have become a primary global concern. Thus, the prediction of air pollution is a crucial issue. ANFIS is an artificial intelligence technique consisting of artificial neural networks and fuzzy inference systems, and it is widely used in estimating studies. To obtain effective results with ANFIS, the training process, which includes optimizing its premise and consequent parameters, is very important. In this study, ANFIS training has been performed using three popular metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) for modeling air pollution. Various air pollution parameters which are particular matters: PM<inf>2.5</inf> and PM<inf>10</inf>, sulfur dioxide (SO<inf>2</inf>), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), carbon monoxide (CO), and several meteorological parameters such as wind speed, wind gust, temperature, pressure, and humidity were utilized. Daily air pollution predictions in Istanbul were obtained using these particular matters and parameters via trained ANFIS approaches with metaheuristics. The prediction results from GA, PSO, and DE-trained ANFIS were compared with classical ANFIS results. In conclusion, it can be said that the trained ANFIS approaches are more successful than classical ANFIS for modeling and predicting air pollution.