Neural Computing and Applications

, Volume 31, Issue 10, pp 6249–6259 | Cite as

Neural network modeling of monthly salinity variations in oyster reef in Apalachicola Bay in response to freshwater inflow and winds

  • Duc Le
  • Wenrui HuangEmail author
  • Elijah Johnson
Original Article


Estuarine organisms have varying tolerances and respond differently to salinity. Bottom-dwelling species such as oysters tolerate some change in salinity, but salinity outside an acceptable range will negatively affect their abundance as well as their survival within this sensitive ecosystem. Salinity in the Apalachicola Bay is heavily influenced by freshwater inflow discharged from the Apalachicola River. In this study, artificial neural network (ANN) was applied to correlate the monthly salinity variations at an oyster reef in Apalachicola Bay to the river inflow and wind. Parameters in the ANN were trained until the simulated salinity data correlated well with the observations from 2005 to 2007. Once the model is trained and optimized, the ANN structure is verified comparing the simulated data to the second dataset from 2008–2010. Four neural network training algorithms, including gradient decent, scaled conjugate gradient, quasi-Newton, and Levenberg–Marquardt, have been evaluated. The scaled conjugate gradient algorithm was selected for this study because it provides the best correlation with the value of 0.85. The verified ANN model was applied to investigate the potential impacts of freshwater reductions from upstream river on the salinity in the oyster reef. By comparing the resulting salinity from ANN model simulations to the optimal salinity range for oyster growth, the impacts of freshwater reduction scenarios on oyster growth can be examined.


Neural network Salinity Freshwater inflow Oyster Apalachicola Bay 



This research was funded in part under Award No. NA11SEC4810001 from the National Oceanic and Atmospheric Administration (NOAA) of Environmental Cooperative Sciences Center (ECSC) at Florida Agricultural & Mechanical University (FAMU). The statements and conclusions are those of the authors and do not necessarily reflect the views of NOAA-ECSC or their affiliates.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Altunkaynak and Wang (2011) Estimation of significant height in shallow lakes using the expert system techniques. Expert Syst Appl 39(2012):2549–2559Google Scholar
  2. 2.
    Berrigan ME (1990) Biological and economical assessment of an oyster resource development project in Apalachicola Bay, FL. J Shellfish Res 9:149–158Google Scholar
  3. 3.
    Bishop C (2006) Pattern recognition and machine learning. Springer, BerlinzbMATHGoogle Scholar
  4. 4.
    Camp EV, Pine WE III, Havens K, Kane AS, Walters CJ, Irani T, Lindsey AB, Morris JG (2015) Collapse of a historic oyster fishery: diagnosing causes and identifying paths toward increased resilience. Ecol Soc 20(3):45. CrossRefGoogle Scholar
  5. 5.
    Chen WB, Liu WC, Huang WC, Liu HM (2017) Prediction of salinity variations in a tidal estuary using artificial neural network and three-dimensional hydrodynamic models. Comput Water Energy Environ Eng 6:107–128CrossRefGoogle Scholar
  6. 6.
    Coen LD, Brumbaugh RD, Bushek D, Grizzle R, Luckenback MW, Posey MH, Powers SP, Tolley SG (2007) Ecosystem services related to oyster restoration. Mar Ecol Prog Ser 341:303–307CrossRefGoogle Scholar
  7. 7.
    Demuth H, Beale M (2009) Matlab neural network toolbox user’s guide version 6. The MathWorks Inc., NatickGoogle Scholar
  8. 8.
    Edminston HL, Fahrny SA, Lamb MS, Levi LK, Wanat JM, Avant JS, Wren K, Selly NC (2008) Tropical storm and hurricane impacts on a Gulf coast estuary: Apalachicola Bay, Florida. J Coast Res 55:38–49CrossRefGoogle Scholar
  9. 9.
    Emanuel KA (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688CrossRefGoogle Scholar
  10. 10.
    Harned DA, Newcomb DJ, Hudson ET, Levine JF (1996) Salinity variation in an estuary used for oyster cultivation in Southeastern North Carolina during the passover of the eye of Hurricane Bertha [abs.]. Transactions of the American Geophysical Union 1996 Fall Meeting, December 1996, San Francisco, California, EOS, vol 77, no 46.
  11. 11.
    Haykin S (2009) Neural networks and learning machines: a comprehensive foundation. Prentice Hall, Englewood CliffsGoogle Scholar
  12. 12.
    Huang W, Foo S (2002) Neural network modelling of salinity variation in Apalachicola River. Water Res 36(2002):356–362CrossRefGoogle Scholar
  13. 13.
    Huang WR, Xu B, Chan-Hilton A (2004) Forecasting flows in Apalachicola River using neural networks. Hydrol Process 18(13):2545–2564CrossRefGoogle Scholar
  14. 14.
    Karr JR (1991) Biological integrity: a long-neglected aspect of water resource management. Ecol Appl 1:66–84CrossRefGoogle Scholar
  15. 15.
    Kişi Özgür (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539CrossRefGoogle Scholar
  16. 16.
    Kisi O, Karimi Sepideh, Shiri Jalal, Makarynskyy Oleg, Yoon Heesung (2014) Forecasting sea water levels at Mukho Station, South Korea using soft computing techniques. Int J Ocean Clim Syst 5(4):175–188CrossRefGoogle Scholar
  17. 17.
    La Peyre MK, Gossman B, La Peyre JF (2009) Defining optimal freshwater flow for oyster production: effects of freshet rate and magnitude of change and duration on eastern oysters and Perkinsus marinus infection. Estuar Coasts 32:522–534CrossRefGoogle Scholar
  18. 18.
    Lee JW, Park Sun-Cheon (2016) Artificial neural network-based data recovery system for the time series of tide stations. J Coast Res 32(1):213–224Google Scholar
  19. 19.
    Lee TL, Makarynskyy Oleg, Shao Chen-Chi (2007) A combined harmonic analysis–artificial neural network methodology for tidal predictions. J Coast Res 23(3):764–770CrossRefGoogle Scholar
  20. 20.
    Lenihan HS, Peterson CH (1998) How habitat degradation through fishery disturbance enhances impacts of hypoxia on oyster reefs. Ecol Appl 8:128–140.[0128:HHDTFD]2.0.CO;2 CrossRefGoogle Scholar
  21. 21.
    Livingston RJ, Xufeng N, Lewis JG III, Woodsum GC (1997) Freshwater input to a gulf estuary: long-term control of trophic organization. Ecol Appl 7(1):277–299CrossRefGoogle Scholar
  22. 22.
    Livingston RJ, Lewis FG, Woodsum GC, Niu XF, Galperin B, Huang W, Christensen JD, Monaco ME, Battista TA, Klein CJ, Howell RL IV, Ray GL (2000) Modeling oyster population response to variation in freshwater input. Estuar Coast Shelf Sci 50:655–672CrossRefGoogle Scholar
  23. 23.
    Londhe SN, Deo MC (2004) Artificial Neural Networks for Wave Propagation. J Coastal Res 20(4):1061–1069CrossRefGoogle Scholar
  24. 24.
    Lopez R (2017) OpenNN: Open Neural Networks Library.
  25. 25.
    Luenberger David G (1973) Introduction to linear and nonlinear programming, vol 28. Addison-Wesley, Reading, MAzbMATHGoogle Scholar
  26. 26.
    Mackenzie CL Jr (1970) Causes of oyster spat mortality, conditions of oyster setting beds, and recommendations for oyster bed management. Proc Natl Shellfish Assoc 60:59–67Google Scholar
  27. 27.
    Makarynskyy O, Makarynska Dina, Rayson Matthew, Langtry Scott (2015) Combining deterministic modelling with artificial neural networks for suspended sediment estimates. Appl Soft Comput 35:247–256CrossRefGoogle Scholar
  28. 28.
    Melesse AM, Krishnaswamy Jayachandran, Zhang Keqi (2008) Modeling coastal eutrophication at Florida Bay using neural networks. J Coast Res 24(2A):190–196CrossRefGoogle Scholar
  29. 29.
    NOAA National Ocean Service. Apalachicola Bay Station (2005–2010).,%20FL. Last accessed Mar 2016
  30. 30.
    National Estuarine Research Reserve System (NERRS) (2012) System-wide monitoring program. Data accessed from the NOAA NERRS Centralized Data Management Office Accessed 01 Oct 2016
  31. 31.
    Oczkowski A, Lewis FG, Nixon SW, Edmiston HL, Robinson RS, Chanton JP (2011) Fresh water inflow and oyster productivity in Apalachicola Bay, FL (USA). Estuar Coasts 34(5):993–1005CrossRefGoogle Scholar
  32. 32.
    Petes LE, Brown AJ, Knight CR (2012) Impacts of upstream drought and water withdrawals on the health and survival of downstream estuarine oyster populations. Ecol Evol 2:1712–1724CrossRefGoogle Scholar
  33. 33.
    Rath JS, Hutton PH, Chen L, Roy SB (2017) A hybrid empirical-Bayesian artificial neural network model of salinity in the San Francisco Bay-Delta estuary. Environ Model Softw 93:193–208CrossRefGoogle Scholar
  34. 34.
    Ruhl JB (2005) Water wars, eastern style: divvying up the Apalachicola–Chattahoochee–Flint River basin. J Contemp Water Res Educ 131:47–54CrossRefGoogle Scholar
  35. 35.
    Sumich JL (1996) An introduction to the biology of marine life, 6th edn. Wm. C. Brown, Dubuque, IA, pp 255–269Google Scholar
  36. 36.
    Tsai CP, Lee TL (1999) Back-propagation neural network in tidal-level forecasting. J Waterw Port Coast Ocean Eng ASCE 125(4):195–202CrossRefGoogle Scholar
  37. 37.
    Twichell DC, Andrews BD, Edminston HL, Stevenson WR (2007) Geophysical mapping of oyster habitats in a shallow estuary, Apalachicola Bay, FL. United States Geological Survey Open-File Report 2016-1381.
  38. 38.
    Twichell D, Edmiston L, Andrews B, Stevenson W, Donoghue J, Poore R, Osterman L (2010) Geologic controls on the recent evolution of oyster reefs in Apalachicola Bay and St. George Sound, Florida. Estuar Coast Shelf Sci 88(3):385–394. CrossRefGoogle Scholar
  39. 39.
    USACE (1998) Water allocation for the Apalachicola–Chattahoochee–Flint (ACF) River Basin, main report of the draft environmental impact statement. Fed Regist 63:53023–53024Google Scholar
  40. 40.
    Wang H, Huang W, Harwell MA, Edminston L, Johnson E, Hsieh P, Milla K, Christensen J, Stewart J, Liu X (2008) Modeling oyster growth rate by coupling oyster population and hydrodynamic models for Apalachicola Bay, FL. Ecol Model 2011:77–89CrossRefGoogle Scholar
  41. 41.
    Wilbur DH (1992) Associations between freshwater inflows and oyster productivity in Apalachicola Bay, FL. Estuar Coast Shellfish Sci 35:179–190CrossRefGoogle Scholar
  42. 42.
    Wilberg MJ, Livings ME, Barkman JS, Morris BT, Robinson JM (2011) Overfishing, disease, habitat loss, and potential extirpation of oysters in upper Chesapeake Bay. Mar Ecol Prog Ser 436:131–144. CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringFAMU-FSU College of EngineeringTallahasseeUSA
  2. 2.School of the EnvironmentFlorida A&M UniversityTallahasseeUSA
  3. 3.NOAA-Environmental Cooperative Science CenterFlorida A&M UniversityTallahasseeUSA

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