## 4 Application and Results  
### Setting up the Standalone Models  
The statistical parameters of the collected dataset are presented in Table 1. A considerable wide domain of data values can be observed in Table 1 (e.g., max of NOx = 0.46 mg/L and max of TDS = 1950.0). This issue implies that before importing the data to the standalone models (MLP and CCNN), they should be standardized between specific ranges (e.g., from 0 to 1). As can be seen from Table 1, the pH was the highest correlated parameter to the DO concentration (correlation coefficient (CC) = 0.760). It was followed by NOx (CC = 0.554) and then by WT with a negative value of the correlation coefficient (CC = \(-\)0.544), which denoted the reverse effect of WT on the DO concentration. Based on the coefficient of variation, the WT data were least dispersed, and the NOx data were most sporadic. Several combinations for setting up the standalone models (MLP and CCNN) regarding the input vectors (e.g., consisting of five chemical characteristic parameters of chloride (Cl), nitrogen oxides (NOx), total dissolved solid (TDS), potential of hydrogen (pH), and water temperature (WT)) can be created. Bear in mind that the only value in the output layer always corresponded to the DO concentration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Variable** & **Unit** & **Min** & **Max** & **Median** & **Mean** & **SD** & **CV** & **CC** \\ \hline \multirow{4}{*}{Input} & Cl & mg/L & 40.000 & 850.000 & 200.000 & 247.649 & 164.973 & 0.666 & 0.418 \\  & NOx & mg/L & 0.001 & 0.460 & 0.072 & 0.089 & 0.087 & 0.978 & 0.554 \\  & TDS & & 134.000 & 1950.000 & 559.500 & 642.362 & 355.163 & 0.553 & 0.394 \\  & pH & Standard Units & 6.100 & 8.220 & 7.180 & 7.148 & 0.421 & 0.059 & 0.760 \\  & WT & \({}^{\circ}\)C & 8.980 & 32.220 & 23.510 & 232.378 & 5.211 & 0.022 & \(-\)0.544 \\ \hline Output & DO & mg/L & 0.090 & 11.480 & 5.920 & 5.485 & 2.565 & 0.468 & 1.000 \\ \hline \hline \end{tabular} Note: CV: the coefficient of variation, and CC: the coefficient correlation between inputs and DO.  
\end{table}
Table 1: Summary of statistics parameters of input and output variables (\(n=232\)).  
Figure 5: Location of the study area and the water quality monitoring station.  
In this study, six different input combinations for constructing the models, including five chemical characteristic parameters (combination ID = I) and even one quality parameter (combination ID = V and combination ID = VI), based on the highest positive and negative values of CC in Table 1 were constructed (see Table 2).  
Constructing the ANN architecture involves the creation of the ANN topology and training parameters, such as the number of neurons in the hidden layer(s). Looking back at Table 2, the first step of ANN architecture for determining the input combinations was already completed. In the second step, the main structure of ANN in terms of the number of layers should be specified. Based on the reports for the capability of the one hidden layer supervised neural networks in simulating complex phenomena [88], this study adopted a one hidden layer architecture for the standalone models (e.g., MLP and CCNN). Finally, the optimal number of neurons in the hidden layer was determined using the MSE criterion by a trial and error approach (see the third column of Tables 3-5).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**DWT**} & \multirow{2}{*}{**Topology**} & \multicolumn{4}{c}{**Training Phase**} & \multicolumn{4}{c}{**Testing Phase**} \\ \cline{5-10}  & & & **RMSE** & **WI** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** & **WE** & **WI** & \(\mathbf{R^{2}}\) & **MAE** \\  &