## 4 Experiments and Results
### Evaluation of the Flash Flood Forecasting Method
Table 4 shows the mean absolute error, root mean squared error and the coefficient of determination (\(R^{2}\)) obtained by the MLP after unfold the river level time series using these parameters. At this point, we can compare the results obtained in this work and results of [PERSON] et al. (2013), where seven different flood prediction models were used, each combining information from different sensors. In their work, [PERSON] et al. (2013) used the MLP to create the models, having a hidden layer containing 12 neurons, but it is not described in detail how they selected the amount of input data for each model. These results are presented in Table 5, where the correlation coefficient (\(R^{2}\)) is not presented in the original work but is calculated from the correlation (\(R\)), by reasons for comparison. As we can see, based on the \(R^{2}\), the results obtained and presented by the approach of this paper surpass five of the models presented in [PERSON] et al. (2013), losing only to the last two models, which use data of more than one sensor. As shown in Table 4, the best results were achieved using the model created by using \(\tau=1\) and \(m=4\), therefore the average river level from \(t-9\) to \(t-5\) and from \(t-4\) to \(t\) are used to forecast the average river level from \(t+1\) to \(t+5\), assuming current time as \(t\). In this way, we continue using this model for the next experiments with SENDI and evaluate the results obtained based on the following measures of performance: sensitivity, precision, specificity and accuracy. These measures are calculated from a confusion matrix presented in Figure 10. Also, using \(m=4\) determines that every Tier 1 node must store at least the last 30 consecutive own readings. This is necessary to feed the local forecast model. In this way, tier 1 nodes must be able to store at least 30 floats values and be able to execute a simple MLP with 1 hidden layer with 3 neurons. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline \(\tau=1\) and \(m=3\) & 12.9704 & 22.9193 & 0.6813 \\ \(\tau=17\) and \(m=4\) & 31.9467 & 48.6781 & 0.0024 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multilayer Perceptron: Mean squared error. Figure 10: Performance measurements calculated by the confusion matrix ([PERSON], 2013). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Description** & \(\mathbf{R}\) & \(\mathbf{R^{2}}\) \\ \hline A & Only rain as input (RN) & 0.5745 & 0.3301 \\ B & Only moisture as input (HM) & 0.2521 & 0.0636 \\ C & Only water flow as input (WF) & 0.8512 & 0.7245 \\ D & RN + HM & 0.9713 & 0.9434 \\ E & HM + WF & 0.8914 & 0.7946 \\ F & RN + WF & 0.9891 & 0.9783 \\ G & RN + HM + WF & 0.9912 & 0.9825 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MLP correlation and coefficient of determination ([PERSON] et al., 2013). The simulation was performed again, although this time with attention being paid to the results of the forecasts. A forecasting model created using the training data was embedded in one of the sensors and fed with readings about the river level during the simulation. The confusion matrices of the embedded model, including averages ranging from \(t+1\) to \(t+5\) (red alert) and from \(t+6\) to \(t+10\) (yellow alert), are shown in Tables 6 and 7. According to [32], these measurements have inherent features which are the total sensitivity of the samples where the results are in a really positive category (i.e., positive truths). Precision is obtained from the total number of examples that can be classified as positive but are not always so; in other words, they are negative truths because specificity is the opposite of sensitivity. This involved classifying as negative, the examples which really are negative (false positives) and the accuracy will make it possible to analyze what method is needed to provide an appropriate classification of a piece of equipment.