## 5 Results and Discussion  
The \(R^{2}\) Score, \(MSE\), and \(MAE\) were measured for both FFNN and LSTM network-based models for each wetland water level prediction, and the results are presented in Table 4. The results confirmed that the LSTM had performed better than FFNN in predicting the water levels of the wetlands in all three performance indices considered for the comparison. The performance of both prediction types (i.e., tradition and temporal) for all wetlands considered for the study is present in Figure 5. It presents similar patterns in both FFNN and LSTM networks' performances against each performance index. Both NNs similarly treated each wetland without any bias. Hence, it confirmed that there is no specific relationship between the water level data of a particular wetland and a given NN type (i.e., LSTM or FFNN). Moreover, a detailed analysis of each wetland's FFNN performance confirmed no difference in the degree of correlation between independent variables selected for investigation with the water level among wetlands. The performance difference between LSTM- and FFNN-based models for all wetlands is substantial. The higher performance of LSTM over FFNN concludes that the temporal relationship between the daily water level is much stronger than that between the identified independent variables and water level. However, the number of data points may be inadequate to learn the composite relationship between the seven independent variables and the water level in the FFNN approach. The highest number of data points used for the investigation is 1887 from the Kimbulawala Bridge wetland. As per the thumb rule, it is required to have at least a few 100,000 data points to learn the relationship, especially for seven input variables, successfully. Hence, increasing the volume of training data can improve the prediction performance of FFNN. The NN prediction performances (\(R^{2}\)) were further analyzed against the training data volume of the respective wetland. In order to make a better graphical comparison, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wetland**} & \multicolumn{2}{c}{\(R^{2}\) **Score**} & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{MAE} \\  & **FFNN** & **LSTM** & **FFNN** & **LSTM** & **FFNN** & **LSTM** \\ \hline Kimbulawala Bridge & 0.1685 & 0.8118 & 0.0212 & 0.0017 & 0.0931 & 0.0249 \\ Kotte Canal & 0.2076 & 0.8786 & 0.0112 & 0.0004 & 0.0668 & 0.0155 \\ Kotte North & 0.1478 & 0.8345 & 0.0082 & 0.0011 & 0.0548 & 0.0216 \\ Parliament Lake & 0.1530 & 0.6368 & 0.0173 & 0.0069 & 0.0961 & 0.0371 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The water level prediction performances. Figure 5: Representation of water level prediction performances. normalized values of each field are calculated as presented in Table 5, and Figure 6 illustrates the performance variation viz. training data volume. The normalization will not affect the performance comparison since it is only used to analyze the relative shape of performance pattern variation. As per the graphical illustration of Figure 6, both FFNN and LSTM prediction models followed a similar pattern (only the shape of variation but not performance values) in the normalized performance. Hence, it confirmed both NNs endeavored to discover the hidden relationships (i.e., two types of relationships) for water level prediction. Moreover, although two kinds of relationships considered for the study (i.e., temporal and traditional) have provided a different level of performance, both have a similar degree of information within all wetland data. Therefore, both NNs extracted two types of relationship information from each wetland data in an almost similar ratio. This is confirmed by having normalized performance patterns with the same shape. However, as per Figure 5, it could not find a strong relationship between the amount of training data and prediction accuracy between wetlands.