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HABs & Neural Networks

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  1. HABs & Neural Networks SESSION 5. Fisheries, marine protectedareas, populationoutbursts, biodiversityshifts Artificial neural network approach to population dynamics of Harmful Algal Blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia. Carles Guallar, Margarita Fernández-Tejedor, Maximino Delgado and Jorge Diogène carlesguallar@gmail.com Barcelona, 29 November 2013

  2. HABs & Neural Networks Alfacs Bay (Ebro Delta) Karlodiniumspp. Pseudo-nitzschiaspp.

  3. HABs & Neural Networks Input layerHiddenlayer Output layer Variable 1 Variable 2 Forecast Variable 3 Variable 4 Variable 5 Characteristics: - Feedforward neural network - Sigmoidfunction - Backpropagationwithmomentumterm and flat spot elimination

  4. HABs & Neural Networks Environmental &Phytoplankton 40.65 Latitude N 40.70 40.75 Meteorological Ebro Riverflowrates 0.55 0.60 0.65 0.70 0.75 Longitude E

  5. HABs & Neural Networks Unique data set

  6. HABs & Neural Networks Quantitativedetectionlimit 3.1 Presence > 3.1 Prediction Cells L-1 Phytoplankton counts Classification < 3.1 Absence

  7. HABs & Neural Networks Log10 (Karlodiniumspp.) - Deepwatertemperature (5thprev. week) - Windgust (3rdprev. week) - Irradiance (8th prev. week) - Atmosfericpressure (Log10, 5thprev. week) - Ebro Riverflowrate (Log10, 5thprev. week) 5 previousweeks Lag (weeks) Log10 (Pseudo-nitzschiaspp.) - Deepwatertemperature (14thprev. week) - Windvelocity (10thprev. week) - Watercolumnsalinity (6th prev. week) - Atmosfericpressure (Log10, 13thprev. week) - Ebro Riverflowrate (Log10, 1stprev. week) 5 previousweeks Lag (weeks)

  8. HABs & Neural Networks One-stepweekAbsence-Presencemodels Karlodinium Pseudo-nitzschia Misclassification error (%) Error characteristics Absence Error characteristics Presence

  9. HABs & Neural Networks One-stepweekPredictionmodels Karlodinium Pseudo-nitzschia Coefficient of determination (R2)

  10. HABs & Neural Networks Neural InterpretationDiagram Absence-Presencemodels Karlodiniummodel Presence Absence Pseudo-nitzschiamodel Presence Absence

  11. HABs & Neural Networks Neural InterpretationDiagram Predictionmodels Karlodiniummodel Log10(Cells L-1) Pseudo-nitzschiamodel Log10(Cells L-1)

  12. HABs & Neural Networks ConnectionWeightApproach Absence-Presencemodels Predictionmodels Karlodiniummodels Pseudo-nitzschiamodels

  13. HABs & Neural Networks ConnectionWeightApproach Biological vs Environmental variables Absence-Presence Prediction Karlodinium Karlodinium Pseudo-nitzschia Pseudo-nitzschia

  14. HABs & Neural Networks Conclusions: Neural networkmodelsweredevelopedtopredictPseudo-nitzschiaspp. and Karlodiniumspp. ThepopulationdynamicsforPseudo-nitzschiaspp. and Karlodiniumspp. were similar forthewholeecosystem. Thebigsize of the neural networkmodelshighlightsthecomplexity of thephytoplanktondynamicsin AlfacsBay. Environmental variables are importantfactorsto drive phytoplanktondynamics in AlfacsBay.

  15. HABs & Neural Networks Thankyouverymuch. • Aknowledgments: • Sistema de Observación y Alerta de Proliferación de Microalgas Nocivas en Zonas de Producción Acuícola Marina (PURGADEMAR; IPT-2011-1707-310000). • Programa de seguiment de la qualitat de les aigües, mol·luscs i fitoplanctontòxic a les zones de producció de mariscdellitoralcatalà de la DGPiAM.