Air pollution can lead to various diseases. Especially, Sulfur dioxide (SO2) and Particulate Matter (PM10 ) concentrations in the air directly affect air pollution. Therefore, the estimation of and substances in the air plays an important role in air pollution prediction. In this study, for the data received from the Ministry of Environment and Urbanization air monitoring center, the and pollutants are estimated based on past data with the extreme-learning-adaptive fuzzy extraction system method. Excessive learning algorithms in prediction problems both work fast and can reach low error values. On the other hand, the adaptive neural-fuzzy inference system offers an effective convergence by handling uncertain and incomplete data. Within the scope of the study, estimated and actual values are visualized with figures. The results of extreme learning algorithms are graphed with the error values they reach. Extreme learning-adaptive neural-fuzzy inference system; It is compared with ELM, KELM, CSELM, CDELM, and SELM algorithms. As a result of the comparison, KELM and ELM-ANFIS in terms of RMSE metric for give the best results close to each other with 0.1151 and 0.1155 values, respectively. For , it has been determined that the ELM-ANFIS showed the best performance with 0.0842 RMSE and 0.8171 R2 metric values.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.