## 3 Results
### Relationship between Size-Fractionated Phytoplankton and Environmental Factors  
The correlation between the phytoplankton biomass and the environmental factors in relation to phytoplankton size structure (Figure 5) showed a direct influence of freshwater discharge on phytoplankton biomass of all sizes (Figure 5a). In contrast, an increase in water temperature negatively affected phytoplankton biomass of all sizes (especially net chl _a_) (Figure 5b). An increase in salinity positively influenced net chl \(a\) in the beginning but salinity negatively influenced phytoplankton biomass of all sizes (Figure 5c). An increase in the surface and bottom \(\Delta\)T positively affected total chl \(a\) and net chl \(a\) (Figure 5d). An increase in the surface and bottom \(\Delta\)S had a positive impact across all sizes of phytoplankton biomass, but high surface and bottom \(\Delta\)S negatively influenced net chl \(a\) (Figure 5e). An increase in transparency positively influenced total chl \(a\) and nano chl \(a\) (Figure 5f). Increasing PAR and DS negatively influenced total chl \(a\) and net chl \(a\) (Figure 5g,h). An increase in SR had a positive influence on phytoplankton biomass of all sizes (Figure 5i). The influence of nutrients was dependent on size classes (Figure 5i-l), except for \(\text{PO}_{4}^{3-}\), which negatively influenced all sizes of phytoplankton biomass (Figure 5m). Increasing DIN/DIP (phosphorous restriction) positively influenced total chl \(a\) and nano chl \(a\) but negatively influenced net chl \(a\) (Figure 5n).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Total chl _a_** & **Net chl _a_** & **Nano chl _a_** \\ \hline SSE & 0.0003 & 0.0001 & 0.0002 \\ RMSE & 0.0173 & 0.0119 & 0.0138 \\ R\({}^{2}\) & 0.9952 & 0.9976 & 0.9957 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results for the validation of the artificial neural network model.  
Figure 4: Comparison between measured and predicted values of chlorophyll \(a\) for each size class from (**a**) site A, (**b**) site B, and (**c**) site C.  
Figure 5: Relationship between input parameters (environmental factors) and predicted chlorophyll \(a\) values in the ANN modeling analyses. (**a**) fresh water discharge (dichnge), (**b**) water temperature (temperature), (**c**) salinity, (**d**) surface and bottom temperature difference (\(\Delta\)T), (**e**) surface and bottom salinity difference (\(\Delta\)S), (**f**) transparency, (**g**) photosynthetically active radiation (PAR), (**h**) Duration of sunshine, (**i**) solar radiation, (**j**) nitrite + nitrate (NO\({}_{2}\)\({}^{-}\) + NO\({}_{3}\)\({}^{-}\)), (**k**) ammonium (NH\({}_{4}\)\({}^{+}\)), (**l**) dissolved silicate (DSi), (**m**) phosphate (PO\({}_{4}\)\({}^{3-}\)), and (**n**) dissolved inorganic nitrogen to dissolved inorganic phosphorus ratio (DIN/DIP).  
The influence (contribution level) of each environmental factor on size-fractionated phytoplankton biomass (Figure 6) showed that for total chl \(a\) the greatest influence came from SR, followed by water temperature, surface and bottom \(\Delta\)T, PO43-, transparency, PAR, DS, freshwater discharge, and surface and bottom \(\Delta\)S (Figure 6a). For net chl \(a\), the greatest influence came from water temperature, followed by surface and bottom \(\Delta\)T, SR, transparency, DIN/DIP, NH4+, DSi, DS, and PAR (Figure 6b). For nano chl \(a\), the greatest influence came from DIN/DIP, followed by PO43-, DSi, SR, salinity, NH4+, and freshwater discharge (Figure 6c).