## 3 Results and Discussion
### Prediction of HM Concentrations in Stormwater  
The results also indicate that it is possible to develop a model for assessing stormwater HM concentrations on the basis of precipitation characteristics, catchment parameters, and weather conditions, including visibility. The model proposed in the study can have practical applications to urban catchments. Additionally, the model makes it possible to examine the impact and interactions between variables analysed, both for rainfall and snowmelt events. These issues were investigated in many research studies; however, they focused on homogeneous catchments [46; 48]. Yet, with respect to urban catchments, a large number of problems have not been tackled. The model proposed allows analysis of the impact of many different phenomena (e.g., deposition of airborne pollutants and their washing off the ground surface) and their variability, which depends on land use type. Comparison of simulation results and literature data shows that the ANN model proposed in the study accounts for nonlinear variables, which contributes to its predictive capacity (Table 4, Figure 5). The parameters that characterise the structure of MLP models, namely number of neurons, activation functions in the hidden and output layers, are shown in Table 5. The values of measures of the model fit (R, MAE, RMSE) to experimental data are also given in the table. Based on the data from Table 5, it can be seen that for individual networks, a number of neurons in the hidden layer ranges from 28 to 35. Additionally, the analyses demonstrate that the number of neurons obtained for individual MLP models is not greater than that recommended [25]. The results show that for Ni and Zn, selected independent variables ensure satisfactory predictive capacities of HMC mathematical models--R\({}^{2}>0.90\) (Figure 5). With regard to Cr and Pb, the fit of simulated values to the measured ones is also satisfactory (0.83 \(<\) R\({}^{2}<\) 0.86). In addition, Figure 5 shows that the smallest scatter is found for concentrations lower than 0.08 mg\(\cdot\)dm\({}^{-3}\) (Cr) and 0.4 mg\(\cdot\)dm\({}^{-3}\) (Pb). With respect to Cu, good predictive abilities of the model are observed only up to the value of approx. 0.2 mg\(\cdot\)dm\({}^{-3}\). The value of R\({}^{2}\) = 0.256 for Cd shows poor fit between ANN predictions and measurements, especially for concentrations higher than 0.03 mg\(\cdot\)dm\({}^{-3}\) (Figure 5). The analyses indicate (Table 4) that the pattern of variation in HM concentrations for catchments with diversified land use is much more complex than in homogeneous catchments [46]. Additionally, in heterogeneous catchments, mechanisms affecting the quality and quantity of stormwater are far more complex. Consequently, taking into account simulation results, it is difficult to specify variables that decidedly determine HM concentrations. The concentrations of the analysed HM are affected to the greatest extent by the following values: G, P\({}_{(10)}\), T\({}_{\text{me}}\), w\({}_{\text{me}}\) and A (Table 3). It is necessary to conduct further investigations and select adequate computational models to identify key independent variables. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ni**} & \multicolumn{2}{c}{**Cu**} & \multicolumn{2}{c}{**Cr**} & \multicolumn{2}{c}{**Zn**} & \multicolumn{2}{c}{**Pb**} & \multicolumn{2}{c}{**Cd**} \\ \hline
**Variable** &  
Figure 5: Predicted versus observed HM concentrations in stormwater (validation step).