## 5 Results and Discussion
Hence, it confirmed the degree of relationship information embedded in the given wetland data is much more important than the available volume of data for accurate water level prediction. Since LSTM-based prediction provided a higher performance, LSTM was selected for further analysis. The comparison between the actual and predicted water levels by the LSTM network for each wetland is presented in Figure 7. Except for a few days in the Parliament Lake wetland, for all other wetlands, the LSTM model was able to provide greater prediction accuracy. Although, in some applications, the \(R^{2}\) score failed to represent a direct comparison performance of actual vs. predicted but represents a correlation. However, in this application, the comparison graphs confirmed that \(R^{2}\) was able to directly compare actual vs predicted water levels together with other performance indices that _MSE_ and _MAE_ selected for the analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wetland** & **Training Data Volume** & **\(R^{2}\) Value of FFNN** & **\(R^{2}\) Value of LSTM** & \begin{tabular}{c} **Normalized Training Data** \\ **Volume** \\ \end{tabular} & \
\begin{tabular}{c} **Normalized \(R^{2}\)** \\ **Value of FFNN** \\ \end{tabular} &
\begin{tabular}{c} **Normalized \(R^{2}\)** \\ **Value of LSTM** \\ \end{tabular} \\ \hline Kimbulawala & 1500 & 0.1685 & 0.7917 & 0.3870 & 0.2572 & 0.2520 \\ Kotte Canal & 1200 & 0.2076 & 0.8786 & 0.3096 & 0.3168 & 0.2796 \\ Kotte North & 800 & 0.126 & 0.8345 & 0.2064 & 0.1923 & 0.2656 \\ Parliament Lake & 375 & 0.153 & 0.6368 & 0.0967 & 0.2335 & 0.2026 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Normalized performances and training data volume. Figure 6: Normalized prediction performances (\(R^{2}\) Score) and training data volume analysis. A comparison of actual vs predicted water levels in the graphs for all wetlands showed that the LSTM model could accurately predict average and extreme values (i.e., peaks and valleys). It confirms the greater generalization capability of the LSTM-based prediction model. A few variations can be found in the graphs, especially for the Kotte North wetland water level prediction. As stated, the relationship between water levels in a wetland is highly non-linear and complex. Hence, the achieved prediction performance of LSTM is outstanding. These results indirectly confirm the robustness of the prediction model against UQ, especially in extreme values (i.e., peaks and valleys) of water levels. Further, the loss curves of the LSTM network during the training period were analyzed and presented in Figure 8. It will provide insights into NN learning behavior, capability, and speed. As per the loss curves, Kotte Canal showed the best convergence behavior, which was reflected in the results of the \(R^{2}\) score comparison in Table 6. It concludes that the amount of embedded relationship information within the data set is much more substantial than the training data volume in water level prediction. Figure 7: Actual vs. predicted water level comparison for LSTM-based model of (**a**) Kimbulawala, (**b**) Kotte Canal, (**c**) Kotte North, and (**d**) Parliament Lake. The performance of the proposed model is compared with the performance of the existing studies, as presented in Table 6, although the study is for relationship comparison. Table 6 contains two wetland water level predictions: one urban water flow prediction and one groundwater level prediction performance. All the listed studies used a large volume of training data compared with the current study. In contrast, the proposed LSTM model has achieved substantial results using limited training data. One of the critical limitations of the study is the inadequate amount of data for the analysis. For effective model development, it is required to have at least a few 100,000 data points per wetland. In addition, it was assumed that the independent variable readings did not deviate considerably during that day from the record measurement.