# 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  
This section introduces the results of the long short-term memory (LSTM) model. We tested performance with many feature set variants for the LSTM models. For all the LSTM experiments listed below, a batch size of \(1\) with a learning rate of \(1\times 10^{-3}\), no early stopping set, and a dropout setting of 0.4 were used as they were the most optimal values, yielding the results shown in Tables 4-7. Figures 9-11 show the results of the stacked LSTM models at epochs 250, 380, and 430. Figure 8: Early testing experiments. Blue dots are actual, black is predicted. (**a**) Linear regression model results; (**b**) Random forest model results; (**c**) Multilayer perceptron model results; (**d**) Voting ensemble model results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Mean** & **Mean** & **Root Mean** & **Residual** & \multirow{2}{*}{**Error Rate**} \\  & **Average Error** & **Square Error** & **Square Error** & **Standard Error** & **Error Rate** \\ \hline Linear Regression & 0.08101 & 0.0111 & 0.1057 & 0.0143 & 0.5486 \\ Random Forest & 0.0702 & 0.008 & 0.0925 & 0.0125 & 0.4797 \\ Multilayer Perceptron & 0.0716 & 0.009 & 0.0965 & 0.0131 & 0.5005 \\ Voting Ensemble & 0.0726 & 0.0088 & 0.0938 & 0.0127 & 0.4867 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of four ML models executed as initial experiments. \begin{table}
\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
1 & Stacked LSTM [Alabama Plant TROPOMI \\  & input only] Epochs 250 & 0.1414 & 0.0424 & 0.2060 & 0.0303 & 0.4685 \\
2 & Stacked LSTM [Alabama Plant TROPOMI \\  & input only] Epochs 380 & 0.1546 & 0.0443 & 0.2106 & 0.0310 & 0.4788 \\
3 & Stacked LSTM [Alabama Plant TROPOMI \\  & input only] Epochs 430 & 0.1535 & 0.0491 & 0.2217 & 0.0326 & 0.5040 \\
4 & TROPOMI/MERRA-2 Monthly/Date inputs] & 0.1332 & 0.0375 & 0.1938 & 0.0285 & 0.4406 \\ Epochs 250 & 0.1546 & 0.0443 & 0.2106 & 0.0310 & 0.4788 \\
5 & TROPOMI/MERRA-2 Monthly [Abbama Plant \\  & input only] Epochs 380 & 0.1535 & 0.0491 & 0.2217 & 0.0326 & 0.5040 \\
3 & Stacked LSTM [Alabama Plant \\
4 & TROPOMI/MERRA-2 Monthly/Date inputs] & 0.1332 & 0.0375 & 0.1938 & 0.0285 & 0.4406 \\ Epochs 250 & 0.1546 & 0.0371 & 0.1927 & 0.0284 & 0.4383 \\
5 & TROPOMI/MERRA-2 Monthly [Abbama Plant \\  & Epochs 380 \\  & Stacked LSTM [Alabama Plant \\
6 & TROPOMI/MERRA-2 Monthly [Abbama Plant \\  & Epochs 430 \\
7 & TROPOMI/MERRA-2 Daily [Abbama Plant \\  & Epochs 380 \\
10 & Stacked LSTM [Alabama Plant \\
11 & Stacked LSTM [Alabama Plant \\  & Epochs 380 \\
10 & Stacked LSTM [Alabama Plant \\
12 & TROPOMI/MERRA-2 Daily [Abbama Plant \\  & Epochs 430 \\
10 & Stacked LSTM [Alabama Plant MERRA-2 \\  & daily input only] Epochs 250 \\
11 & Stacked LSTM [Alabama Plant MERRA-2 \\  & daily input only] Epochs 380 \\
12 & Stacked LSTM [Alabama Plant MERRA-2 \\  & daily input only] Epochs 380 \\
13 & TROPOMI/MERRA-2 Weekly/Date inputs] & 0.1359 & 0.0355 & 0.1884 & 0.0277 & 0.4284 \\ Epochs 250 & 0.1546 & 0.0438 & 0.2094 & 0.0308 & 0.4761 \\
14 & TROPOMI/MERRA-2 Weekly [Abbama Plant \\  & Stacked LSTM [Alabama Plant \\  
\begin{table}