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HABs & Neural Networks. SESSION 5. Fisheries , marine protected areas , population outbursts , biodiversity shifts. Artificial neural network approach to population dynamics of Harmful Algal Blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo- nitzschia . - PowerPoint PPT Presentation
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Artificial neural network approach to population dynamics of Harmful Algal Blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia.
HABs & Neural Networks
Carles Guallar, Margarita Fernández-Tejedor, Maximino Delgado and Jorge Diogè[email protected]
Barcelona, 29 November 2013
SESSION 5. Fisheries, marine protected areas, population outbursts, biodiversity shifts
Alfacs Bay (Ebro Delta)
Karlodinium spp. Pseudo-nitzschia spp.
HABs & Neural Networks
HABs & Neural Networks
input hidden 1 hidden 2 output
t- 1
t- 2
t- 3
t- 4
t- 5
t
Input layer Hidden layer Output layer
Variable 1
Variable 5
Variable 4
Variable 3
Variable 2
Forecast
Characteristics:- Feedforward neural network- Sigmoid function- Backpropagation with momentum term and flat spot elimination
0 . 5 5 0 . 5 6 0 . 5 7 0 . 5 8 0 . 5 9 0 . 6 0 . 6 1 0 . 6 2 0 . 6 3 0 . 6 4 0 . 6 5 0 . 6 6 0 . 6 7 0 . 6 8 0 . 6 9 0 . 7 0 . 7 1 0 . 7 2 0 . 7 3 0 . 7 4 0 . 7 54 0 . 5 5
4 0 . 5 6
4 0 . 5 7
4 0 . 5 8
4 0 . 5 9
4 0 . 6
4 0 . 6 1
4 0 . 6 2
4 0 . 6 3
4 0 . 6 4
4 0 . 6 5
I AC I A
C AE A
P A
0.55 0.60 0.65 0.70 0.75
Longitude E
40.65
40.70
40.75
Latit
ude
N
HABs & Neural Networks
Environmental & Phytoplankton
Meteorological Ebro River flow rates
HABs & Neural Networks
Unique data set
HABs & Neural Networks
Quantitative detection limit3.1
Phytoplanktoncounts Classification
Prediction> 3.1
< 3.1
Presence
Absence
Cells L-1
HABs & Neural Networks
Log10 (Karlodinium spp.)
Lag (weeks)
5 previous weeks
Log10 (Pseudo-nitzschia spp.)
Lag (weeks)
5 previous weeks
- Deep water temperature (5th prev. week)
- Wind gust (3rd prev. week)
- Irradiance (8th prev. week)
- Atmosferic pressure (Log10, 5th prev. week)
- Ebro River flow rate (Log10, 5th prev. week)
- Deep water temperature (14th prev. week)
- Wind velocity (10th prev. week)
- Water column salinity (6th prev. week)
- Atmosferic pressure (Log10, 13th prev. week)
- Ebro River flow rate (Log10, 1st prev. week)
HABs & Neural Networks
Karlodinium Pseudo-nitzschia
Misclassification error (%)
One-step week Absence-Presence models
Error characteristicsAbsence
Error characteristicsPresence
HABs & Neural Networks
Karlodinium Pseudo-nitzschia
Coefficient of determination (R2)
One-step week Prediction models
HABs & Neural Networks
Neural Interpretation DiagramAbsence-Presence models
Karlodinium model
Pseudo-nitzschia model
PresenceAbsence
PresenceAbsence
HABs & Neural Networks
Neural Interpretation DiagramPrediction models
Pseudo-nitzschia model
Karlodinium model
Log10(Cells L-1)
Log10(Cells L-1)
HABs & Neural Networks
Connection Weight Approach
Pseu
do-n
itzsc
hia
mod
els
Kar
lodi
nium
mod
els
Absence-Presence models Prediction models
HABs & Neural Networks
Connection Weight ApproachBiological vs Environmental variables
Absence-Presence Prediction
KarlodiniumKarlodinium
Pseudo-nitzschiaPseudo-nitzschia
HABs & Neural Networks
Conclusions:
1. Neural network models were developed to predict Pseudo-nitzschia spp. and Karlodinium spp.
2. The population dynamics for Pseudo-nitzschia spp. and Karlodinium spp. were similar for the whole ecosystem.
3. The big size of the neural network models highlights the complexity of the phytoplankton dynamics in Alfacs Bay.
4. Environmental variables are important factors to drive phytoplankton dynamics in Alfacs Bay.
Thank you very much.
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 fitoplancton tòxic a les zones de producció de marisc del litoral català de la DGPiAM.
HABs & Neural Networks