Swearson Presentation

8/14/2019 Swearson Presentation

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Harmful Algal Bloom forecasting feasibility of

Monterrey Bay using remote sensing Data and Data

Extraction Software, Weka.

SARP 2009

William Swearson


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Objective

Attempt to determine thefeasibility of forecasting

algal blooms in

Monterey Bay. Successful forecasting

models have been developed

for the East Coast, Gulf of

Mexico as well as in the

coastal waters near HongKong.

Monterey Bay presents

certain difficulties that make it

unique to forecast.mas.arc.nasa.gov/gallery.html


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What are Algal Blooms? Algae are unicellular,

sometimes multicellular,autotrophic organisms.

Blooms are a rapidincrease in algae in a

marine environment. Under the right

conditions; favorabletemperature, solar

radiation, nutrientconcentration, windspeed, and tidal flushingalgae blooms can

flourish (Lee, Huang, Dickman,Ja awardena, 2002 .


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So why study Algae Blooms?

Forecasting algae

blooms=world peace


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So why study Algae Blooms?

Massachusetts PSP closures as of

June 25, 2009

http://www.whoi.edu/page.do?pid=23996&tid=441&cid=69708&ct=61&article=13371

Algal blooms have

increased their global

distribution, their

frequency, duration, and

severity of their effects. Some of these algal

species are called

HABs, Harmful Algal

Blooms. Produce a toxin called

domoic acid.


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Methodology

Forecasting is beingattempted in MontereyBay using direct andindirect measurements.

I wanted to look at thepossibilities offorecasting by looking atMASTER data.

This type projectrequired a large volumeof data at different lagtimes.

Environmental Sample

Processor, or "ESP."


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Methodology However we ran into

unexpected problemsduring MASTER datacollection

Much was still learned

despite our setbacks.Eagles may soar, but

weasels dont getsucked into jet enginesSteven Wright

During the rest of theflight Clint and Idiligently thought aboutwhat else could be

done.


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Methodology Dr. John Ryan and Dr. Nick

Clinton were able to provideme with data from the boatthat took directmeasurements of MontereyBay.

Excel files contained data onchlorophyll reflectance, seasurface temps, salinity andvarious bands.

These would be pumpedinto data extractingsoftware, Weka, to look forcorrelations between thedata sets.


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Setup

Y=G(x) Y, chlorophyll

Divided into classes, i.e. {high, med,low}

X, MASTER Bands and Ship Data Subjectively chosen

Bands 2,12,15,18,22,45,48,49, Salinity, Temp

G, some unknown function Neural Network

Multi-Layer Perceptron

Classification Tree J48

SimpleCart


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Weka is a collectionof machine learningalgorithms for datamining tasks.

Uses varioustechniques Classification Trees

Neural Networking The algorithms can

be applied directly toa dataset.

Weka the program is named after this

curious New Zealand bird.

Methodology


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What is a Neural Network? Artificial Neural

Networks, ANNs,use the sameconcept the brainuses to make

decisions. y=G(x)

A non-linear datamining techniqueused to discovernon-linearrelationships withchlorophyll.

Hidden Layer

Output:

Chlorophyll

Input:

MASTER,temp,

salinity etc


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Looked at data fromthe Friday July, 24thflight

4 days before aAlgal Bloom inMonterey Bay.

Provided data intowhich

environmentsHABs thrived in(truthing).

Selected variousbands that hadpatterning of linearregression or arelation betweenchlorophyll.

Scatter Plots of Chlorophyll vs.

MASTER BANDS


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Neural Networking

-Used the subjectively

chosen plots and all bands.-Subjectively Chosen bands

had a higher accuracy

compared to all bands.


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How it works

SimpleCart

- Simple Classification And

Regression Tree

Function Selection

Modifier- pruningAccuracy


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Weka Classification

Tree Model


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J48~ 91% accuracy on the input data sety


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Conclusion

Developing the learning algorithms neededfor making the forecasting models is

energetically demanding.

The timing of the datasets is important for

developing a predictive model.

The model can show which variables are

predictive (e.g. MASTER thermal and NIR

bands). Prediction is feasible, but more data and

testing are needed.


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Acknowledgments

John Ryan and Nick Clinton for supplying mewith data.

Nick Cliton again for helping me with the data. NSERC

SARP

Sesame Street for the a letter W and thenumber 6.


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References

Gower, King, Borstad and Brown. 2009.Detection of intense plankton bloomsusing 709nm band of the MERIS imaging spectrometer. International Journal of

Remote Sensing. Vol. 26, No. 9.

Lee, Huang, Dickman, Jayawardena.Neural network modeling of coastal algal

blooms. 2002. Ecological Modelling 159, 179-201.

NCCOS. HAB Ecological Forecasting. Center for Sponsored Coastal OceanResearch. 2009. Center for Sponsored Coastal Ocean Research. Retrieved July

2009.

http://www.cop.noaa.gov/stressors/extremeevents/hab/current/ecoforecasting.htm

l

Oceanus. Researchers Successfully Forecast 2008 Red Tide. 2009. Woods

Hole Oceanic Institute. Retrieved July 2009.

http://www.whoi.edu/oceanus/viewArticle.do?id=47406

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Swearson Presentation