# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.1 LSTM Results
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **Experiments** & \begin{tabular}{c} **Mean Average** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Mean Square** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Root Mean** \\ **Square Error** \\ \end{tabular} & \begin{tabular}{c} **Residual** \\ **Standard Error** \\ \end{tabular} & \begin{tabular}{c} **Error** \\ **Rate** \\ \end{tabular} \\ \hline \multirow{4}{*}{1} & Stacked LSTM [All 289 Power \\  & Plants Average [TROPOMI/Date \\  & inputs] 380 epochs \\  & Stacked LSTM [All 289 Power \\  & Plants Average [TROPOMI/Date \\  & inputs] 430 epochs \\  & Stacked LSTM [All 289 Power \\  & Plants Average [TROPOMI/Date \\  & inputs] 800 epochs \\ \hline \hline \end{tabular}  \ \begin{tabular}{c} **Mean Average** \\ **Error** \\ **Error** \\ \end{tabular}  & 0.1604 & 0.0366 & 0.1914 & 0.0201 & 0.6098 \\  &
\begin{tabular}{c} **R**eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee  
Figure 10: Graph showing the LSTM model [380 epochs] capture of EPA NO\({}_{2}\) actual values over the year using a single power plant. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Results**} & \multicolumn{4}{c}{**Hyperparameters**} \\ \hline \multirow{3}{*}{**Number**} & \multirow{2}{*}{**Experiments**} & **Mean** & **Mean** & **Root Mean** & **Residual** & **Error** & **Coefficient** & **Regularization** & **Epsilon** & **Value** \\  & & **Average** & **Eror** & **Square** & **Error** & **Standard** & **Error** & **Rate** & **Coefficient** & **Regularization** & **Parameter (C)** & **Epsilon** & **Value** \\ \hline \multirow{4}{*}{7} & SVR GridSearch Best & & & & & & & & & & & \\  & Params [Alabama Plant & & & & & & & & & & \\  & TROPOMI/MERRA-2 & 0.0572 & 0.0062 & 0.0792 & 0.0113 & 0.4047 & 0.01 & 0.1 & 0.0001 & \\  & Weekly/MCD19A2/ & & & & & & & & & & \\  & Date inputs/ & & & & & & & & & & \\  & SVR GridSearch Best & & & & & & & & & & \\  & Params [Alabama Plant & & & & & & & & & \\  & TROPOMI/MERRA- & 0.0537 & 0.0055 & 0.0745 & 0.0106 & 0.3804 & 0.01 & 0.1 & 0.001 & \\  & 2/ MCD19A2/Date inputs & & & & & & & & & & \\  & SVR Gridsearch Best & & & & & & & & & \\  & Params [All 289 Power & 0.01402 & 0.0003 & 0.0177 & 0.0017 & 0.2264 & 0.0001 & 1000 & 0.01 \\  & input only & & & & & & & & & \\  & SVR GridSearch Best & & & & & & & & & \\  & Params [All 289 Power & 0.0095 & 0.0001 & 0.0121 & 0.0012 & 0.1552 & 0.01 & 0.1 & 0.005 \\  & Plants RROPOMI/ & & & & & & & & & \\  & Date inputs & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 9: Graph showing the LSTM model [250 epochs] capture of EPA NO\({}_{2}\) values over the year using a single plant. Figure 9 largely underestimated EPA NO\({}_{2}\). This figure corresponds to LSTM experiment #1 (Table 4). The error rate of 46.85% ranked best for the first three experiments using only TROPOMI NO\({}_{2}\) as input and 250 epochs but ranked fifth worst between all experiments and different feature set combinations in Table 4. This was primarily due to the lack of input features used for training. The error rate increased by increasing the epochs to 380 and 430, which was indicative of its inability to further learn any patterns in the EPA NO\({}_{2}\) data. Figure 10 offers a model that is not over or underfitting while capturing some of the actual EPA NO\({}_{2}\) data trends (peaks, declines) during the year. This figure corresponds to LSTM experiment #2 (Table 4). Regarding learning patterns for extreme values found in EPA NO\({}_{2}\) data, Figure 10's stacked LSTM model showed more generalization than Figures 9 and 11 based on the line fit for training and testing sets. The evaluation performed on the train and test datasets in Figure 10 had an error rate of 43.83% for the test set and 39.8% for the training set. These results indicate the model is not overfitting; otherwise, the training error rate would be much lower, and the test set larger. Figure 11 corresponds to LSTM experiment #3 (Table 4).