## 4 Proposed Experimental Results and Comparative Performance  
### Dataset Description and Forecast Performance Measures  
Our studied field is Puli Township located in Central Taiwan as shown in Figure 7a. There is a metropolitan (Taichung City) and several power plants and petrochemistry complexes to the west of Puli. Puli is a mountain basin with river outlets to the west part. To make our research practical and verifiable to the public, we chose the dataset of real PM\({}_{2.5}\) concentration and meteorological data values to perform comparative analysis of the predictive models, According to the PM\({}_{2.5}\) dataset maintained by Taiwan Environmental Protection Administration (EPA), Puli is notorious for its high air pollution rank in the list of all EPA supersites [35]. Therefore, we chose the hourly PM\({}_{2.5}\) dataset available at Puli supersite ([[https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx](https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx)]([https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx](https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx)), accessed on 16 December 2021). Figure 7b shows the basin geography of Puli Township and the location of the EPA supersite. The supersite is located in the central Puli downtown which is the most populated area with many shops, restaurants, and temples nearby. In addition to local sources, the air pollution from the western metropolitan drifts through the river valley into Puli basin. The EPA supersite applies the beta attenuation monitoring (BAM) technique for PM\({}_{2.5}\) measurement. The BAM employs the energy absorption of beta radiation by suspended particles extracted from the air flow. The attenuation caused by suspended particles is exponentially dependent on the particle mass in the sample. For the features to be used as the explanatory variables in our parametric models, we obtained the hourly meteorological dataset from the Taiwan Central Weather Bureau ([[http://e-service.cwb.gov.tw/HistoryDataQuery/](http://e-service.cwb.gov.tw/HistoryDataQuery/)]([http://e-service.cwb.gov.tw/HistoryDataQuery/](http://e-service.cwb.gov.tw/HistoryDataQuery/)), accessed on 16 December 2021), which tallies all the raw features we need, namely, temperature, relative humidity, wind speed and direction, and precipitation. The time span of the PM\({}_{2.5}\) and the meteorological datasets is between 2008 and 2019. The data for the early nine years (from 1 January 2008 to 31 December 2016) are designated as the long-term training set. The remaining three years (from 1 January 2017 to 31 December 2019) are used as the test set for evaluating the performance of competing models. The seven days prior to each test day are used as the short-term training set for the corresponding test.  
To evaluate the forecast accuracy, we adopt three performance measures, namely, the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute  
Figure 7: Location of the studied field. (**a**) Puli Township and western plausible pollution sources. (**b**) Basin geography of Puli Township. The EPA supersite marked by a red star is located in the central Puli downtown.  
percentage error (MAPE), which have been broadly used in the literature and they are defined as follows.  
\[\text{RMSE}=\left(\frac{1}{T}\sum_{t=1}^{T}(\hat{y}_{t}-y_{t})^{2}\right)^{\frac {1}{2}}, \tag{7}\]  
\[\text{MAE}=\frac{1}{T}\sum_{t=1}^{T}|\hat{y}_{t}-y_{t}|, \tag{8}\]  
\[\text{MAPE}=\frac{1}{T}\sum_{t=1}^{T}|\hat{y}_{t}-y_{t}|/y_{t}, \tag{9}\]  
where \(y_{t}\) and \(\hat{y}_{t}\) are the observed PM\({}_{2.5}\) value at time \(t\) and the corresponding predicted value, and \(T\) is the number of observed PM\({}_{2.5}\) records in the evaluation period.  
The computation platform for our experiments is a personal computer with a 3.6 GHz CPU and 32 GB RAM, the programs were codified using Python 3.6.8 and the Scikit-learn package.