Monitoring System Of Phytoplankton Blooms By Using Unsupervised Classifier And Time Modelling
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1 Monitoring System Of Phytoplankton Blooms By Using Unsupervised Classifier And Time Modelling Kevin Rousseeuw, IFREMER/LISIC Emilie Caillault, LISIC Alain Lefebvre, IFREMER Denis Hamad, LISIC
2 Summary Marel-Carnot Station General scheme Hybrid K-means / Hidden Markov Model Unsupervised Classification Building HMM Representation Results Conclusion
3 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
4 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
5 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
6 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
7 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
8 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
9 Marel-Carnot Station Located in the harbor of Boulogne-sur-Mer, France It is a strong area of fishing, coastal strip, pressures from «La Liane», common toxic species, But where is Boulogne-sur-Mer?
10 Marel-Carnot Station 19 data signals 16 physics characteristics every 20 minutes (Fluorescence, Turbidity, Corrected Dissolved Oxygen, Temperature, ) 3 nutrients levels every 12h (Nitrate, Phosphate, Silicate) Representation of 6 mains parameters
11 Marel-Carnot Station The Fluorescence is us indicator of bloom. The bloom are not cyclical and the level is different by years. Representation of the Fluorescence
12 Marel-Carnot Station Year Percent Percent age of age of data data with at with at Number least least of data one five paramet paramet er ers missing missing 105, ,
13 General Scheme
14 Hybrid K-mean / Hidden Markov Model
15 Unsupervised Classification Using the fast K-means algorithm (Hartigan-Wong 1979) Algorithm: if number of data > 20,000 points select randomly 20,000 points K-means Using centers in the 2nd K-means
16 Building HMM Representation The HMM is noted λ=(k, K2, π, A, B) K the number of classes (first K-means) K2 the number of symbols in the codebook (second K-means) π of size K, defines the initial probability distribution A of size n*n, defines the transition matrix B of size n*k2, defines the emissions matrix
17 Results Time period: Parameters used: Nutrients (Nitrate, Phosphate, Silicate) Turbidity Corrected Dissolved Oxygen The Fluorescence is used for the visual validation
18 Results of classification (K=2) Detection productive and non-productive periods Distribution of data in each class Temporal change of the fluorescence for the year 2008
19 Results of classification (K=5) Ideally, the environmental states would be: prebloom, bloom, post-bloom, no productive and rare events It is possible to label 2 environmental states within a given bloom (colors green and cyan)
20 Results of classification (K=5) The representation of the Principal Component of the class «green» and «cyan» The structural parameters are: Fluorescence, Oxygen, Salinity, Conductivity and the Wind Class green Class blue
21 Result of prediction (K=5) Each new data is labelling on the codebook (knn). This labelling and the HMM are using with the Viterbi algorithm. Percentage of same labelling by the clustering method and the time modelling for each year is the following: Year Recogn ition Rate
22 Result of prediction (K=5) This figure illustrates the state labelling on the fluorescence signal for year 2009 A unique bloom is detected, so the system is able to detect if one species is dominant
23 Conclusion The monitoring system allows to: Understand the environmental state (parameter release, event precedent release) of a bloom of phytoplankton species or group Predict a possible harmful bloom Refine the measurement campaigns in critical period
24 Thank you Do you have any questions?
Hybrid hidden Markov model for marine environment monitoring
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