# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 5 Experiments
### One-Hour-Ahead Forecasting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Metric** & **LR** & **FFN** & **LSTM** & **GRU** & **LSTM-4** \\ \hline \multirow{4}{*}{Cerrillos} & R2 & 0.566 & 0.656 & 0.708 & **0.712** & 0.710 \\ \cline{2-6}  & RMSE & 12.94 & 11.51 & 10.62 & **10.53** & 10.57 \\ \cline{2-6}  & MAE & 7.08 & 5.20 & **4.42** & 4.63 & 4.67 \\ \hline \multirow{4}{*}{Cerro Navia} & R2 & 0.619 & 0.662 & 0.811 & 0.802 & **0.816** \\ \cline{2-6}  & RMSE & 15.59 & 14.67 & 10.96 & 11.23 & **10.82** \\ \cline{2-6}  & MAE & 7.36 & 5.36 & **3.96** & 3.82 & 4.07 \\ \hline \multirow{4}{*}{ElBosque} & R2 & 0.522 & 0.641 & 0.668 & **0.688** & 0.675 \\ \cline{2-6}  & RMSE & 15.58 & 13.50 & 12.98 & **12.59** & 12.85 \\ \cline{2-6}  & MAE & 8.33 & 6.07 & **5.57** & 5.89 & 5.69 \\ \hline \multirow{4}{*}{Independencia} & R2 & 0.329 & 0.624 & 0.619 & 0.656 & **0.667** \\ \cline{2-6}  & RMSE & 12.06 & 9.03 & 9.09 & 8.64 & **8.50** \\ \cline{2-6}  & MAE & 7.22 & 4.63 & 4.48 & 4.33 & **4.31** \\ \hline \multirow{4}{*}{LaFlorida} & R2 & 0.407 & 0.574 & 0.663 & 0.671 & **0.673** \\ \cline{2-6}  & RMSE & 13.12 & 11.12 & 9.89 & 9.77 & **9.75** \\ \cline{2-6}  & MAE & 7.80 & 5.37 & **4.61** & 5.31 & 5.18 \\ \hline \multirow{4}{*}{Las Condes} & R2 & \(-0.595\) & \(-0.404\) & 0.451 & 0.485 & **0.489** \\ \cline{2-6}  & RMSE & 15.91 & 14.93 & 9.33 & 9.04 & 9.00 \\ \cline{2-6}  & MAE & 8.92 & 6.70 & 4.82 & 4.73 & 4.79 \\ \hline \multirow{4}{*}{P.O’Higgins} & R2 & 0.498 & **0.678** & 0.677 & 0.665 & 0.674 \\ \cline{2-6}  & RMSE & 12.69 & **10.16** & 10.17 & 10.37 & 10.22 \\ \cline{2-6}  & MAE & 6.97 & **4.60** & 4.71 & **4.60** & 4.91 \\ \hline \multirow{4}{*}{Pudahuel} & R2 & 0.538 & 0.658 & **0.779** & 0.777 & 0.759 \\ \cline{2-6}  & RMSE & 15.13 & 13.03 & **10.46** & 10.50 & 10.93 \\ \cline{2-6}  & MAE & 8.47 & 4.50 & **3.98** & 4.51 & 4.42 \\ \hline \multirow{4}{*}{Puente Alto} & R2 & 0.277 & 0.357 & 0.398 & **0.461** & **0.461** \\ \cline{2-6}  & RMSE & 19.73 & 18.60 & 18.01 & **17.04** & **17.04** \\ \cline{2-6}  & MAE & 10.64 & 10.55 & 8.79 & 8.70 & **8.69** \\ \hline \multirow{4}{*}{Quilicura} & R2 & 0.590 & 0.689 & 0.710 & 0.718 & **0.720** \\ \cline{2-6}  & RMSE & 11.01 & 9.59 & 9.26 & 9.14 & **9.11** \\ \cline{2-6}  & MAE & 6.10 & 4.44 & **4.07** & 4.08 & 4.12 \\ \hline \multirow{4}{*}{Talagante} & R2 & 0.370 & 0.532 & **0.587** & 0.573 & 0.581 \\ \cline{2-6}  & RMSE & 14.06 & 12.11 & **11.38** & 11.58 & 11.47 \\ \cline{2-6}  & MAE & 7.21 & 4.97 & 4.68 & **4.45** & 4.66 \\ \hline \multirow{4}{*}{Average} & R2 & 0.375 & 0.515 & 0.643 & 0.655 & **0.657** \\ \cline{2-6}  & RMSE & 14.35 & 12.57 & 11.10 & 10.95 & **10.93** \\ \cline{1-1} \cline{2-6}  & MAE & 7.83 & 5.67 & **4.92** & 5.00 & 5.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metrics of space-time predictions considering 11 stations, of 1 h in the future. Bold font indicates best results. network was able to predict the peak of PM\({}_{2.5}\) that usually occurs around 9 am ([8]) due to the high traffic. In general, we note that the network managed to reasonably predict pollution, especially during the hours when the people are most exposed (i.e., from 7 am to 9 pm).