1 forecasting streamflow by artificial neural networks, arma models and implementation of mapserver...

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Forecasting Streamflow by Artificial Forecasting Streamflow by Artificial Neural Networks, ARMA Models and Neural Networks, ARMA Models and Implementation of Mapserver for Ria Implementation of Mapserver for Ria FormosaFormosa

MM.Sc. Defense Presentation.Sc. Defense PresentationMarch 29March 29thth 2007 2007

Mehmet Cüneyd Demirel

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The The PresentationPresentation will cover; will cover;• IntroductionIntroduction• Methodology: Methodology:

Flow Forecasting SchemeFlow Forecasting Scheme

• Study Area: Study Area: Alportel and Pracana Basins, Alportel and Pracana Basins,

PortugalPortugal

• Data: Data: Flow Height and StreamflowFlow Height and Streamflow

• ResultsResults• Final ConclusionsFinal Conclusions• Selected Publications Selected Publications • ReferencesReferences

IN A NUTSHELL

1. What we did?

2. How we did?

3. We really did? Then So What? Any concrete results?

4. Whys and future projection

5. Any peer-reviewed products?? Publish or perish..

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Introduction:Introduction: What we did?What we did?

We tried to predict We tried to predict the flow in the river the flow in the river accuratelyaccurately by by previous previous precipitation and precipitation and other climate other climate information. information.

Vamos a presentar Vamos a presentar modelo de modelo de prediccion de flujo prediccion de flujo de los rios con la de los rios con la informacion de informacion de recogida de aguas recogida de aguas pluviales. pluviales.

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Introduction:Introduction: Three types of lookThree types of look

•Our Problem:

Flow forecastFlow forecast. Not enough,

More practical and more accuratemore accurate

•Why is this (flow) necessary?Why is this (flow) necessary?

•Preferred Model:

Black Box, Artificial Neural Networks

•Why?

Simple, practical and a huge literature

*10.12010.120 papers only in 10 years; 1995 to 2005 (Liao and Wen 2007).

•But what is Artificial Neural Networks? *Data driven, historical data is enough then it is economic

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Artifical Neural Networks;Artifical Neural Networks;

Artificial Neural Networks (ANNs) are loosely based on brain cell behavior.

Current research into the brain's physiology is very limited.

We do not know, how neurons work or even what constitutes intelligence in general. [HIDDEN..HIDDEN..]

But the key point is;

The mechanisms for how man learns and reacts to everyday experiences.

1 st cell model by McCulloch et al., (1943).

Basically: Three main elements are

Dendrites (Input) –Neruon (Hidden) –Axon (Output) and connections (synapses)

*Neuron fires or stays calm

We will give a simple example laterinspired by neurons

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Other Models;Other Models;

•We used:

-A Stochastic Model: Auto-Regressive Moving Average Model (ARMA)

-Least Squares Method

-A Process-based Model: Soil and Water Assessment Tool (SWAT), a physical model. The results were provided by Venâncio et al., (2006)

•Our OBJECTIVEs are to:

>> predict river flow accurately by previous climate data (temp, humidity, rain),

>> benchmark ANNs with other models,

Hint:

One distinctive aspect of this studyOne distinctive aspect of this study was the inclusion of the cluster analysis in evaluation of the ANN model performance.In accordiance with these objectives, we defined two hypotheses

77

Focus of this talk: Hypothesis 1Focus of this talk: Hypothesis 1

H1: H1: Streamflow forecasting by Streamflow forecasting by artificial neural networks can be artificial neural networks can be more accurate than stochastic more accurate than stochastic and process-based models.and process-based models.

TRUE or FALSE?TRUE or FALSE?

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Study Area: Alportel River Study Area: Alportel River BasinBasin

Data (time series and maps) were provided by governmental institute; Sistema Nacional de Informação de Recursos Hídricos (SNIRH) which is responsible of the river basins over the country

Bodega and Picota gauge stations

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Study Area: Thematic MapsStudy Area: Thematic Maps

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Application: Flow Application: Flow ForecastingForecasting

The region (Algarve) is arid, dry conditions are The region (Algarve) is arid, dry conditions are dominant dominant

The rivers have usually intermittent (temporary) The rivers have usually intermittent (temporary) characteristicscharacteristics

Model Runs at 3 time domains: Model Runs at 3 time domains:

Hourly, daily, and monthlyHourly, daily, and monthly In hourly domain, we collected available flow height In hourly domain, we collected available flow height

data and related climate data such as temperature, data and related climate data such as temperature, precipitation, and humidityprecipitation, and humidity (2001-2006) (2001-2006)..

Many numerical experiments were conducted to data Many numerical experiments were conducted to data and significant results were given in this study. and significant results were given in this study.

Optimum ModelOptimum Model is is higlighted for hourly domain. for hourly domain.

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Data: Hourly DomainData: Hourly Domain

0 0.5 1 1.5 2 2.5 3

x 104

0

50PORTUGAL HOURLY DATA~(2001.10.01-2006.8.16)~~PICOTA and BODEGA

Temp

0 0.5 1 1.5 2 2.5 3

x 104

0

50

100Humid

0 0.5 1 1.5 2 2.5 3

x 104

0

20

40Prep

0 0.5 1 1.5 2 2.5 3

x 104

0

2

4

6FlowH

Used for training the modelUsed for testing Hint: High correlation

0 5 10 15 20-0.2

0

0.2

0.4

0.6

0.8

Lag

Sam

ple

Auto

correla

tion

Auto Correlation BODEGA [Flow Height]

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Results: Hourly DomainResults: Hourly Domain

Note: Exp: Experiment, ANN= artificial neural network; FH=Flow height; P= Precipitation, T=Temperature, H=Humidity and h: hourly.

10004-16-1908020x103FHtTt-1, Ht-1, Pt-1, FHt-1ANN-hFHExp-V

3003-14-1908020x103FHtHt-1, Pt-1, FHt-1ANN-hFHExp-IV

3002-9-1908020x103FHtPt-1, FHt-1ANN-hFHExp-III

10x1010x103322--44--119080908020x1020x1033FHFHttPPtt--1,1, FHFHtt--11ANNANN--hFHhFHExpExp--IIII

3001-6-1908020x103FHtFHt-1ANN-hFHExp-I

EpochsNetworkStructure

TestTrainingOutputInputModelID

Note: Exp: Experiment, ANN= artificial neural network; FH=Flow height; P= Precipitation, T=Temperature, H=Humidity and h: hourly.

10004-16-1908020x103FHtTt-1, Ht-1, Pt-1, FHt-1ANN-hFHExp-V

3003-14-1908020x103FHtHt-1, Pt-1, FHt-1ANN-hFHExp-IV

3002-9-1908020x103FHtPt-1, FHt-1ANN-hFHExp-III

10x1010x103322--44--119080908020x1020x1033FHFHttPPtt--1,1, FHFHtt--11ANNANN--hFHhFHExpExp--IIII

3001-6-1908020x103FHtFHt-1ANN-hFHExp-I



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Results: Hourly Performance TableResults: Hourly Performance Table

Note: Exp: Experiment, MSE= Mean Squared Error, R2 =coefficient of determination or explained variance and CA= Cluster Analysis.

Not Precise233.5160FailedPassed0.96360.006103Exp-V

Poor74.2180FailedPassed0.78810.036274Exp-IV

Successful54.4840PassedPassed0.9840.0028488Exp-III

Optimum623.6570PassedPassed0.9940.0009277Exp-II

Successful43.1570PassedPassed0.95130.0077394Exp-I

OverallConclusion

Run Time(sec)

K-Means Criteria

CA Criteria

R2MSEID







OverallConclusion

Run Time(sec)

K-Means Criteria

CA Criteria

R2MSEID

Hint: There is a high auto-correlation in hourly Flow Height data (more than 0.9 for lag 1)

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Results: ANN Hourly Performance Results: ANN Hourly Performance GraphsGraphs

•Lets back to the method Lets back to the method to catch the point; >> to catch the point; >> how it works?how it works?

We have 2 inputs;

1) One hour previous precipitation value: P(t-1)

2) One hour previous flow height value: FH(t-1)

We have one output;

1)Flow height: FH(t)

Mean Squared Error for

training part

Test, validation

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Artifical Neural Networks;Artifical Neural Networks;

Training

Test

Network

Structure

Epochs

20x103 9080 2-4-1 10x103

We divided our data into two parts;

-Training

-Model Validation

1.32

Precipitation= [0.21 0.32 0.24 0.18 0.0 .......]

Flow Height = [1.19 1.32 1.31 1.12 1.0 ...... ]

Iterations

Hidden

Output

Input

Hint: Process starts with initial weights, transfer function, and feed back

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Data: Daily DomainData: Daily Domain

0 500 1000 1500 2000 25000

50

100PORTUGAL DAILY DATA~(1990.09.30-1975.10.01)~~PICOTA, FAZ FATO, SANTA CATARINA(Precipitation) and BODEGA(Streamflow)

P1-PICOTA

0 500 1000 1500 2000 25000

50

100

150P2-FAZ FATO

0 500 1000 1500 2000 25000

50

100

150P3-SANTA CATARINA

0 500 1000 1500 2000 25000

50

100

150S1-BODEGA

Hint: Low correlation

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Results: Daily DomainResults: Daily Domain

The results for: one day ahead streamflow prediction by only 1 previous streamflow

Poor in Magnitudes

N/AN/A1388.1SWATModel

Well inMagnitudes

6.3750.1268423.1Exp-IV

OverallConclusion

Run time(sec)

R2MSEID

Poor in Magnitudes

N/AN/A1388.1SWATModel

Well inMagnitudes

6.3750.1268423.1Exp-IV

OverallConclusion

Run time(sec)

R2MSEID

ANN-SWAT Comparison based on Exp-IV forecast

Model Performance Table

Model Architecture TableHint:

Modified ANN structure got the peak flows better

than SWAT

Remarkable Success

0 100 200 300 400 500 600 700 800-500

0

500

1000

1500

2000SWAT-ANN MODEL COMPARISON by PRACANA BASIN DATA EXP-IV Results

Observed

ANN

0 100 200 300 400 500 600 700 8000

200

400

600

800

1000

1200Observed

SWAT

ANN

SWAT

1001-6-17484x103StSt-1ANN-dSExp-IV



1001-6-17484x103StSt-1ANN-dSExp-IV



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Data: Monthly DomainData: Monthly Domain

0 50 100 150 200 250 300 350 4000

200

400MONTHLY DATA~(1959.11.01-1990.9.01)~~PICOTA, FAZ FATO, SANTA CATARINA(Precipitation) and BODEGA(Streamflow)

P1-PICOTA

0 50 100 150 200 250 300 350 4000

200

400P2-FAZ FATO

0 50 100 150 200 250 300 350 4000

200

400P3-SANTA CATARINA

0 50 100 150 200 250 300 350 4000

200

400

600S1-BODEGA

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Results: Monthly DomainResults: Monthly Domain

Very Poor16.12500.170124215.7Exp-V

Not preciseNot precise6.07800.272622409.9Exp-IV

Not precise55.65600.336192202.4Exp-III

Not precise85.21900.305322308.5Exp-II

Not precise9.50000.382392059.6Exp-I

OverallConclusion

Run Time(sec)

R2MSEID

Very Poor16.12500.170124215.7Exp-V

Not preciseNot precise6.07800.272622409.9Exp-IV

Not precise55.65600.336192202.4Exp-III

Not precise85.21900.305322308.5Exp-II

Not precise9.50000.382392059.6Exp-I

OverallConclusion

Run Time(sec)

R2MSEID

Experiment IV: One month ahead flow forecast.

R2 =0.15

ARMA (1,1)

R2 is Low for all Exp.s and Negative values exist in ARMA (not desired for flow process)

ARMA (1,1)

2020

Results: Monthly DomainResults: Monthly Domain

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350

400

450

500

t (month)

Stream

flow

(m

3 /s)

PORTUGAL MONTHLY DATA (Bodega Station)

Streamflow

Estimated Value

•Least Squares Method was relatively more successfull than other models however;

>>We kept ARMA model very simple (p=1, q=1) according to parsimony principle, hence if the order of the model will be increased we expect that the model will get better the magnitudes

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Focus of this talk: Hypothesis 2Focus of this talk: Hypothesis 2

H2: H2: Cluster analysis can be used Cluster analysis can be used successfully in evaluating flow successfully in evaluating flow forecasting models.forecasting models.

TRUE or FALSE?TRUE or FALSE?

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Simple idea behind H2;Simple idea behind H2;

Hint:

In our knowledge; this is the first application of clustering in model evaluation

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Cluster AnalysisCluster Analysis

We seek the same pattern group before and after simulation. It must be robust to pass the criteria.

Before

(Observed values)

After

(Predicted values)

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Results: Clustering for Results: Clustering for validationvalidation

A contrast but this confirmed that only K-means criteria is successfull in model evaluation.







OverallConclusion

Run Time(sec)

K-Means Criteria

CA Criteria

R2MSEID







OverallConclusion

Run Time(sec)

K-Means Criteria

CA Criteria

R2MSEID

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Overall: Better performed Overall: Better performed modelsmodels

Note: Exp: Experiment, FH=Flow height, S: Streamflow, T&E: trial and Error, FF: Feedforward, BP: Backpropagation, Traingda: Gradient descent with adaptive learning rate backpropagation, Trainlm: Levenberg-Marquardt backpropagation, Logsig: Log sigmoid transfer function, Tansig:Hyperbolic tangent sigmoid transfer function and Purelin: Linear transfer function (adapted from Url-3).

EpochsTrainlmTansigFF-BP

Exp-IAlportel/Bodega

SMonthly

EpochsTraingdaTansigFF-BPExp-IV,SWAT

Pracana/Almeirão

Norm.S

EpochsTraingdaLogsigFF-BPExp-IVAlportel/Bodega

S

Daily

EpochsTraingdaLogsigFF-BPExp-IIAlportel/Bodega

FHHourly

Stoppingrule

LearningAlgorithm

TransferFunction

Connection andOptimisation

Method

OptimumModel

River Basin/Station

Datatype

Domain

Note: Exp: Experiment, FH=Flow height, S: Streamflow, T&E: trial and Error, FF: Feedforward, BP: Backpropagation, Traingda: Gradient descent with adaptive learning rate backpropagation, Trainlm: Levenberg-Marquardt backpropagation, Logsig: Log sigmoid transfer function, Tansig:Hyperbolic tangent sigmoid transfer function and Purelin: Linear transfer function (adapted from Url-3).

EpochsTrainlmTansigFF-BP

Exp-IAlportel/Bodega

SMonthly

EpochsTraingdaTansigFF-BPExp-IV,SWAT

Pracana/Almeirão

Norm.S

EpochsTraingdaLogsigFF-BPExp-IVAlportel/Bodega

S

Daily

EpochsTraingdaLogsigFF-BPExp-IIAlportel/Bodega

FHHourly

Stoppingrule

LearningAlgorithm

TransferFunction

Connection andOptimisation

Method

OptimumModel

River Basin/Station

Datatype

Domain

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Finally: Implementation of UMN Finally: Implementation of UMN Mapserver for Ria FormosaMapserver for Ria Formosa

In this thesis, the necessary steps for constructing an end-to-end end-to-end streamflow forecasting systemstreamflow forecasting system were discussed. These steps include the use of MapServer for the organization and visualisation of the available data steps, and methodologies, based on ANN, ARMA and SWAT models for prediction problem.

These steps were applied in different domains (Ria Formosa, a coastal lagoon, Alportel and Pracana river basins) however the modelling scheme can be combined in one water body to have very fast and efficient end-to-end management tool.

end-to-end streamflow end-to-end streamflow forecasting systemforecasting system

Our dream:Our dream:

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ConclusionsConclusions The ANN flow forecasting scheme applied in Alportel River seems to have The ANN flow forecasting scheme applied in Alportel River seems to have

reached encouraging results, particularly in the model for hourly height reached encouraging results, particularly in the model for hourly height vvalues.alues.

The initial success of the ANN-CA models developed for the Alportel River The initial success of the ANN-CA models developed for the Alportel River sub-basin indicates a bright future for further applications in the entire sub-basin indicates a bright future for further applications in the entire Algarve basin or as well as other catchments in Iberian peninsulaAlgarve basin or as well as other catchments in Iberian peninsula

OOverall performance comparison, the criteria of MSE shows that SWAT verall performance comparison, the criteria of MSE shows that SWAT model produced more accurate results than our ANN model but the lack of model produced more accurate results than our ANN model but the lack of magnitudes (peak values) was a significant issue in extreme events like magnitudes (peak values) was a significant issue in extreme events like flood studiesflood studies

The two hypoteses were achieved in this study; (Do you still remember?)The two hypoteses were achieved in this study; (Do you still remember?)

>>ANN can be more accurate in flow forecasting than other models >>ANN can be more accurate in flow forecasting than other models

>>CA can be used for model validation >>CA can be used for model validation

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Fruitful Products during ErasmusFruitful Products during Erasmus

Papers

•Kahya E., and Demirel M. C., 2007: A Comparison of Low-Flow Clustering Methods: Streamflow Grouping. Journal of Engineering and Applied Sciences 2(3): 524-530.

•Demirel, M. C., Kahya E., and Dracup, J. A., 2007: Cluster Analysis of Annual and Seasonal Turkish Streamflow Patterns. Water Resources Research (in review).Others•Demirel M .C., Martins F., Galvão P., and Saraiva S., 2006: Implementation of Web Mapping Tools for Monitoring Water Quality in Ria Formosa Coastal Lagoon using UMN MapServer. 40 th CMOS CONGRESS, Weather, Oceans& Climate, Exploring the Connections. May 29 - June 1, 2006 Toronto, Canada.

•Ganapuram S., Hamidov A., Demirel, M. C., Bozkurt E., Kındap U., and Newton A., 2007: Erasmus Mundus Scholar's Perspective On Water And Coastal Management Education In Europe. International Congress - River Basin Management, March 22-24, 2007 Antalya, Turkey.

•Kahya E., and Demirel M. C., 2007: Evaluation of Multivariate Statistical Methods for Characterizing Annual Streamflow Regimes in Turkey. EGU General Assembly, April 15-20, 2007 Vienna, Austria.

•Kahya E., Demirel M. C., and Piechota T. C. 2007: Spatial Grouping of Annual Streamflow Patterns in Turkey. 27th AGU Hydrology Days, Fort Collins, Colorado, March 19-21, 2007.

•Demirel M. C., and Kahya E., 2007: Hydrological Determination of Hierarchical Clustering Scheme by Using Small Experimental Matrix. 27th AGU Hydrology Days, Fort Collins, Colorado, March 19-21, 2007.

•Demirel M. C., Mariano A. J., and Kahya E., 2007: Performing K-means Analysis to Drought Principal Components of Turkish Rivers. 27th AGU Hydrology Days, Fort Collins, Colorado, March 19-21, 2007.

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Selected ReferencesSelected References

1. Liao, S.H., and Wen, C.H., 2007: Artificial Neural Networks Classification and Clustering Of Methodologies and Applications – Literature Analysis from 1995 to 2005. Expert Systems with Applications (32) 1–11.

2. Venâncio, A., Martins F., Chambel, P. and Neves R., 2006: Modelação Hidrológica da Bacia Drenante da Albufeira de Pracana. V CONGRESSO IBÉRICO SOBRE GESTÃO E PLANEAMENTO DA ÁGUA, Faro-PORTUGAL, 4-8 December.

3. McCulloch, W. S. and Pitts, W., 1943: A Logical Calculus of the Ideas Imminent in Nervous Activity. Bulletin of Mathematical Biophysics, 5:115-133.

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Thank you,Thank you,Funding:Funding:

1) The program ERASMUS and to himself Gerrit Gerritzon (Desiderius Erasmus)2) Istanbul Technical University

My mentors:My mentors:Dr. Flávio Martins (UALG) and Dr. Arthur J. Mariano (UM)

My Committee:My Committee:Drs. Ángel Del Valls Casillas, Alice Newton, Gilliam Glegg, Francisco López Aguayo and Carmen Sarasquete

My Friends and Family!

My Friends and Family!

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