Abstract
Water transparency is commonly used to indicate the combined effect of hydrodynamics and the aquatic environment on water quality throughout a river network. However, how water transparency responds to these indicators still needs to be explored, especially their complicated nonlinear relationship; thus, this study represents an analysis of the Suzhou civil river network. Using an artificial neural network (ANN) hydrological model and a multiple linear model (MLR) with in-situ data between 2013–2019, we investigated the Suzhou River’s sensitivity to the six factors and water transparency, which including flow velocity and data from five categories of water-quality monitoring data: total suspended matter (TSS), water temperature (TE), dissolved oxygen (DO), chlorophyll (Chl) and chemical oxygen demand (COD). The results suggest that the ANN model can achieve better performance than the MLR model. Furthermore, results also show a well-established correlation between enhanced hydrodynamics and improved water transparency when the flow velocity ranged from 0.22 to 0.45 m/s. Overall, COD is a vital factor for the SD prediction because including the COD can see a notable improvement in the ANN model (with a correlation coefficient of 0.918). This study demonstrates that the ANN model with hydrodynamic and water quality parameters can achieve a better prediction of water transparency than other discussed models for a coastal plain urban river network.
Highlights
It appears that the artificial neural network (ANN) model is more accurate and consistent in different subsets since all the values of root mean squared error (RMSE) and mean absolute error (MAE) are similar, and all the correlation coefficients are close to unity, and the performance of this model can be well demonstrated based on RMSE
An artificial neural networks model is proposed for estimating Secchi depth in a plain urban river network using long-term observed data
Through the comparison of results between the ANN model and multiple linear regression (MLR) model, it reveals that the hydrodynamic parameters can be used as effective parameters for Secchi depth (SD) prediction models of the urban river network
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Urbanization has accelerated the degradation of urban aquatic ecosystems, and the associated ecological issues have been recognized in China [1,2,3]. To mitigate these effects and improve urban river health, large-scale water-clearing regulations have been instituted for plain urban river networks, such as the one that empties into the Yangtze. River Delta [4,5]. Water transparency is a commonly used indicator of water quality [6]
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