Abstract
Estimating salinity concentrations in coastal waters allows characterization of the spatial and temporal dynamics of the freshwater/saltwater interface. In Southeast Florida (USA) the saltwater interface is monitored and evaluated for potential impacts to public supply wellfields and biological communities. In this research, a closed-loop autoregressive neural network with exogenous inputs was developed to estimate salinity concentrations at a coastal water quality station (BISCC4) in Biscayne Bay, Florida. The neural network (ANN) is shown to successfully simulate hourly salinity concentrations for years 2015 through 2019. A statistical comparison of simulated concentrations versus observed data demonstrates that the ANN simulates salinity concentration values and trends within acceptable margin of errors (R2 = 0.59, K-G = 0.64, NSE = 0.33, d = 0.86, PBIAS = 1.5%, RSR = 0.82). In its current form, the ANN model performs better in simulating salinity concentrations and trends, than an existing hydrodynamic model. These results have the potential to be applied to other coastal locations in Biscayne Bay where freshwater inputs from inland streams and canals are affecting salinity concentrations.
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Alarcon, V.J., Linhoss, A.C., Kelble, C.R., Mickle, P.F., Bishop, J., Milton, E. (2021). Estimation of Hourly Salinity Concentrations Using an Artificial Neural Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12954. Springer, Cham. https://doi.org/10.1007/978-3-030-86979-3_44
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