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Prediction of Algal Blooms in the Bristol Channel using ANNs

Prediction of Algal Blooms in the Bristol Channel using ANNs. Cardiff University Dr. Bettina Bockelmann-Evans Prof. Binliang Lin, Rakhee Ramachandran, Ma Ning. Aims. Use long term data to establish an ANN to predict occurrence of algal blooms in the Bristol Channel

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Prediction of Algal Blooms in the Bristol Channel using ANNs

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  1. Prediction of Algal Blooms in the Bristol Channel using ANNs Cardiff University Dr. Bettina Bockelmann-Evans Prof. Binliang Lin, Rakhee Ramachandran, Ma Ning

  2. Aims • Use long term data to establish an ANN to predict occurrence of algal blooms in the Bristol Channel • Validate and test the ANN (MATLAB) model to: • achieve acceptable accuracy and • define significance of factors influencing algal blooms (including river discharge)

  3. Initial Data Analysis • Software called winGamma (Cardiff University, Computer Science Department) used for pre-ANN analysis: • Gamma test used to find the most appropriate variables giving a smooth model (minimum noise) and to avoid over-training • M-test conducted to find best number of input variables

  4. Gamma test and M-test Gamma scatter plot of North Cornwall – Experiment 1 Unique data points v Gamma of North Cornwall – Experiment 1

  5. Prediction of Algal Blooms using Artifical Neural Networks (ANNs) ANNs are data driven modelling inspired by the biological nervous system Same structure as in neurons of human brains Ability to interpret complicated or imprecise data Suitable for this problem – can deal with many input parameters Three layers, i.e. input, hidden and output layer

  6. (Artificial) Neural Networks

  7. Prediction of Algal Blooms using Artifical Neural Networks (ANNs) The network allow signals to flow from the input layer to the output layer and vice versa according to the network chosen The input variables should be chosen carefully - large number of input variables increases noise rather than giving an accurate solution Feed forward network is generally used for prediction Learning is the process of determining weights for the input parameters Supervised learning is widely used technique

  8. Procedure Flow Chart

  9. Input Variables

  10. Empiricial Method Spreadsheet2007 Data

  11. Break-points

  12. Statistical Analysis of ANNs Results Experiment 1: 0.8694 0.7407 0.52 0.58 Experiment 2: 0.8690 0.6792 0.54 0.69 Experiment 3: 0.9954 0.8993 0.12 0.24 Experiment 4: 0.9819 0.8472 0.17 0.38 Experiment 5: 0.9950 0.9912 0.12 0.08 Experiment 6: 0.9950 0.8708 0.12 0.32 Experiment 7: 0.7143 0.5171 0.95 0.57 Experiment 8: 0.7573 0.6801 0.56 0.49 Experiment 9: 0.9646 0.9023 0.31 0.20 Experiment 10: 0.9897 0.8046 0.22 0.32 Experiment 11: 0.9950 0.9823 0.11 0.12 Experiment 12: 0.9949 0.8623 0.12 0.30

  13. Predicted and observed Chlorophyll-α concentration for training data set at North Devon site - Experiment 3 (9 Par., 8-day lag)

  14. Predicted and observed Chlorophyll-α concentration for testing data set at North Devon site - Experiment 3 (9 Par.)

  15. Relative Importance of Variables -Experiment 3 (North Devon, 9 Par.)

  16. Statistical Analysis of ANNs Results Experiment 1: 0.8694 0.7407 0.52 0.58 Experiment 2: 0.8690 0.6792 0.54 0.69 Experiment 3: 0.9954 0.8993 0.12 0.24 Experiment 4: 0.9819 0.8472 0.17 0.38 Experiment 5: 0.9950 0.9912 0.12 0.08 Experiment 6: 0.9950 0.8708 0.12 0.32 Experiment 7: 0.7143 0.5171 0.95 0.57 Experiment 8: 0.7573 0.6801 0.56 0.49 Experiment 9: 0.9646 0.9023 0.31 0.20 Experiment 10: 0.9897 0.8046 0.22 0.32 Experiment 11: 0.9950 0.9823 0.11 0.12 Experiment 12: 0.9949 0.8623 0.12 0.30

  17. Predicted and observed Chlorophyll-α concentration for training data set at Bideford Bay site - Experiment 9 (9 Par.)

  18. Predicted and observed Chlorophyll-α concentration for testing data set at Bideford Bay site - Experiment 9 (9 Par.)

  19. Relative Importance of Variables –Experiment 9 (Bideford Bay)

  20. Including River Discharge -Scores for this Input Parameter

  21. Relative Importance of Variables –North Cornwall, (10 Par., incl. rivers)

  22. Relative Importance of Variables –NW Cornwall, (10 Par. incl. rivers)

  23. Conclusions • An ANN was built using the existing input data to predict algal blooms in the Bristol Channel • Best results when use of 8-day time lag – Chl-a concs of earlier days important • Results include percentage contribution of input parameters to Chl-a concs • Different variables are relevant at each site • River discharge found to be an important input parameter

  24. Future Work • Use of long term data (including new data set) to improve model predictions • Use of real values for input parameters instead of scores • Include nutrient concentrations as input parameters

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