# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.4 XGBoost Results  
This section introduces the XGBoost model results. Several hyperparameters were set across all experiments listed in Table 9. A maximum depth of 15 to minimize overfitting and 1000 estimators to boost the model's learning complexity were set. A subsample of 0.7 to sample 70% of the training data before growing additional trees and an \"eta\" value of 0.1 to minimize the default step size shrinkage and avoid overfitting were used. A repeated K-Fold was used to find the best performance for each experiment. Three folds were provided for the cross validator to resample, each with three repetitions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Experiments** & **Mean** & **Mean** & **Root Mean** & **Residual** & **Error Rate** \\  & **Average Error** & **Square Error** & **Square Error** & **Square Error** & **Standard Error** & **Error Rate** \\ \hline
1 & XGBoost [Alabama Plant & 0.0933 & 0.0159 & 0.1262 & 0.0180 & 0.6445 \\  & TROPOMI input only] & 0.0933 & 0.0159 & 0.1262 & 0.0180 & 0.6445 \\  & XGBoost [Alabama Plant & & & & & \\
2 & TROPOMI/MERRRA-2 & 0.0542 & 0.0051 & 0.0718 & 0.0102 & 0.3669 \\  & Monthly/Date inputs] & & & & & \\  & XGBoost [Alabama Plant & & & & & \\
3 & TROPOMI/MERRRA-2 & 0.0587 & 0.0060 & 0.0778 & 0.0111 & 0.3974 \\  & Monte inputs] & & & & & \\  & XGBoost [Alabama Plant & & & & & \\
4 & TROPOMI/MERRRA-2 & 0.071 & 0.008 & 0.094 & 0.013 & 0.484 \\  & Daily/Date inputs] & & & & & \\  & XGBoost [Alabama Plant & & & & & \\
5 & TROPOMI/MERRRA-2 & 0.0726 & 0.009 & 0.096 & 0.013 & 0.493 \\  & Daily/MCD19A2/ & & & & & \\  & Date inputs] & & & & & \\  & XGBoost [Alabama Plant & & & & & \\
6 & MERRA-2 Daily/ & 0.071 & 0.008 & 0.093 & 0.013 & 0.479 \\  & Date inputs] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test set validation results of an XGBoost model for the two study scenarios. Figure 26: Random forest models’ feature importance using a single power plant (Alabama) dataset. The correlation plot of actual and predicted EPA NO\({}_{2}\) values are shown in Figures 28 and 29. Both graphs reveal a moderately positive relationship, indicating a good line fit between actual and predicted values in Table 9's #2 and #9 experiments. Figure 30 displays the averaged EPA NO\({}_{2}\) by day across all 289 power plants in the U.S. Although the test set line generally matched the overall trend of actual values, there was much over and underestimation in the predicted testing values. The model underestimated certain days (day 160), but values not as extreme were captured well across the overall EPA NO\({}_{2}\) trend. This model was biased toward a certain range of EPA NO\({}_{2}\) values between 0.08 and 0.10. Figure 31 displays a feature importance graph that the XGBoost model deemed the most important based on the feature's correlation score with EPA NO\({}_{2}\). This model had different feature importance than RF and ranked MERRA-2 predictors as the most important, TROPOMI, and time features the least. This model determined that MERRA-2 features were significantly correlated with EPA NO\({}_{2}\). Figure 31 represents experiment #2 (Table 9). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Experiments** & **Mean** & **Mean** & **Root Mean** & **Residual** & **Error Rate** \\  & **Average Error** & **Square Error** & **Square Error** & **Square Error** & **Standard Error** & **Error Rate** \\ \hline \multirow{4}{*}{7} & XGBoost [Alabama Plant & & & & & \\  & TROPOMI/MERRA-2 & 0.063 & 0.006 & 0.082 & 0.011 & 0.421 \\  & Weekly/Date inputs] & & & & & \\  & XGBoost [Alabama Plant & & & & & \\  & TROPOMI/MERRA-2 & 0.067 & 0.007 & 0.088 & 0.012 & 0.450 \\  & Weekly/MCD19A2/ & & & & & \\  & Date inputs & & & & & \\  & XGBoost [All’289 Power & & & & & \\  & Plants Average & 0.0112 & 0.0001 & 0.0140 & 0.0014 & 0.1793 \\  & TROPOMI/Date inputs] & & & & & \\ \hline \hline \end{tabular}
\end{table}