Similar to the model, an ANFIS model starts with the function being “mapminmax” applied to normalize the input values based on the minimum and maximum values. The function from fuzzy logic toolbox is employed to build Sugeno-type fuzzy “genfis2” inference system from data using subtractive clustering method (Zadeh, 1995). The “anfis” function is used to train the fuzzy inference system and tune the learning parameters. The function is employed to perform fuzzy inference calculations and compute the “evalfis” model’s output (Zadeh, 1995). The function is used to map the results to “mapminmax” original scale. In order to formulate the SVM model, an function from LS-SVMlab toolbox is “initlssvm” employed to define the model (De Brabanter et al., 2011). The model’s parameter is derived from a function, and then the function is used to train the model. “tunelssvm” “trainlssvm” Finally, the simulation is accomplished by using function which calculates the “simlssvm” model’s output (De Brabanter et al., 2011). In this a 10-fold cross-validation (CV) technique is used to evaluate the predictive ability of the different models to identify the optimal model for estimating the PM2.5 concentration. Therefore, the function is used to generate indices for a 10-fold “crossvalind” CV. Then, each candidate model is fitted and cross-validated using these indices and results are averaged to obtain the overall cross validation R2 (CV-R2). Figure S5 provides a schematic diagram of the model building and evaluation process. Figure S5. Schematic diagram of model building and evaluation process used to estimate PM2.5 concentration.
Similar to the model, an ANFIS model starts with the “mapminmax” function being applied to normalize the input values based on the minimum and maximum values. The “genfis2” function from fuzzy logic toolbox is employed to build Sugeno-type fuzzy inference system from data using subtractive clustering method (Zadeh, 1995). The “anfis” function is used to train the fuzzy inference system and tune the learning parameters. The “evalfis” function is employed to perform fuzzy inference calculations and compute the model’s output (Zadeh, 1995). The “mapminmax” function is used to map the results to original scale. In order to formulate the SVM model, an “initlssvm” function from LS-SVMlab toolbox is employed to define the model (De Brabanter et al., 2011). The model’s parameter is derived from a “tunelssvm” function, and then the “trainlssvm” function is used to train the model. Finally, the simulation is accomplished by using “simlssvm” function which calculates the model’s output (De Brabanter et al., 2011). In this a 10-fold cross-validation (CV) technique is used to evaluate the predictive ability of the different models to identify the optimal model for estimating the PM2.5 concentration. Therefore, the “crossvalind” function is used to generate indices for a 10-fold CV. Then, each candidate model is fitted and cross-validated using these indices and results are averaged to obtain the overall cross validation R2 (CV-R2). Figure S5 provides a schematic diagram of the model building and evaluation process. Figure S5. Schematic diagram of model building and evaluation process used to estimate PM2.5 concentration.
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