used as a statistical measure to avoid multicollinearity. VIF values are calculated by using provides a schematic diagram function from package (Naimi, 2015). Figure S4 “vif” “usdm” of data processing and input selection procedure. Figure S4. Schematic diagram of data processing and input selection procedure used to estimate PM2.5 concentration. 5.3 Modelling process The final optimum parameter dataset is used for building BPANN, ANFIS, and SVM models using MATLAB R2014b software. In order to formulate the BPANN model, function is used to normalize the “mapminmax” input values based on the minimum and maximum values. The BPANN network is created by function from the neural network toolbox (Demuth and Beale, 2000). Then, the “newff” network is trained by the function, and the simulation is accomplished by the “train” “sim” function to calculate the final results (Demuth and Beale, 2000). The function “mapminmax” is used to transform the results to original scale.
used as a statistical measure to avoid multicollinearity. VIF values are calculated by using “vif” function from “usdm” package (Naimi, 2015). Figure S4 provides a schematic diagram of data processing and input selection procedure. Figure S4. Schematic diagram of data processing and input selection procedure used to estimate PM2.5 concentration. 5.3 Modelling process The final optimum parameter dataset is used for building BPANN, ANFIS, and SVM models using MATLAB R2014b software. In order to formulate the BPANN model, “mapminmax” function is used to normalize the input values based on the minimum and maximum values. The BPANN network is created by “newff” function from the neural network toolbox (Demuth and Beale, 2000). Then, the network is trained by the “train” function, and the simulation is accomplished by the “sim” function to calculate the final results (Demuth and Beale, 2000). The “mapminmax” function is used to transform the results to original scale.
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