Modeling sulphur dioxide (so2) quality levels of jeddah city using machine learning approaches with meteorological and chemical factors

HIGHLIGHTS

SUMMARY

The MARS technique could be used to determine the best 3 of 21 input variables for the SVR model to build the SVR models as a nonlinear prediction function. By considering the d and NSE to compare the tendency of proposed machine_learning model, while SVR provides the tendency among other models models, the highest d (NSE) was extracted by the proposed machine_learning model, as a second modeling approach. To estimate the ronmental circumstances and pollutants, machine_learning algorithms were used on air influence of environmental circumstances and pollutants, machine_learning algorithms quality considering SO2 as an . . .

 



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