M .Sc. Defense Presentation March 29 th 2007

1. Forecasting Streamflow by Artificial Neural Networks, ARMA Models and Implementation of Mapserver for Ria Formosa Mehmet Cüneyd Demirel M.Sc. Defense Presentation March 29th 2007

2. Introduction • Methodology: Flow Forecasting Scheme • Study Area: Alportel and Pracana Basins, Portugal • Data: Flow Height and Streamflow • Results • Final Conclusions • Selected Publications • References • IN A NUTSHELL • What we did? • How we did? • We really did? Then So What? Any concrete results? • Whys and future projection • Any peer-reviewed products?? Publish or perish.. The Presentation will cover;

3. We tried to predict the flow in the river accurately by previous precipitation and other climate information. Vamos a presentar modelo de prediccion de flujo de los rios con la informacion de recogida de aguas pluviales. Introduction:What we did?

4. Introduction:Three types of look • Our Problem: • Flow forecast. Not enough, • More practical and more accurate • Why is this (flow) necessary? • Preferred Model: • Black Box, Artificial Neural Networks • Why? • Simple, practical and a huge literature • *10.120 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

5. We will give a simple example later Artifical Neural Networks; ArtificialNeural 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 ingeneral. [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 *Neuron fires or stays calm inspired by neurons Dendrites (Input) –Neruon (Hidden) –Axon (Output) and connections (synapses)

6. 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 study was the inclusion of the cluster analysis in evaluation of the ANN model performance. In accordiance with these objectives, we defined two hypotheses

7. Focus of this talk: Hypothesis 1 • H1: Streamflow forecasting by artificial neural networks can be more accurate than stochastic and process-based models. TRUE or FALSE?

8. 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 Study Area: Alportel River Basin

9. Study Area: Thematic Maps

10. Application: Flow Forecasting • The region (Algarve) is arid, dry conditions are dominant • The rivers have usually intermittent (temporary) characteristics • Model Runs at 3 time domains: Hourly, daily, and monthly • In hourly domain, we collected available flow height data and related climate data such as temperature, precipitation, and humidity (2001-2006). • Many numerical experiments were conducted to data and significant results were given in this study. • Optimum Model is higlighted for hourly domain.

11. Used for testing Hint: High correlation Used for training the model Data: Hourly Domain

12. Results: Hourly Domain

13. Results: Hourly Performance Table Hint: There is a high auto-correlation in hourly Flow Height data (more than 0.9 for lag 1)

14. Results: ANN Hourly Performance Graphs • Lets back to the method to catch the point; >> 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

15. Precipitation= [0.21 0.32 0.24 0.18 0.0 .......] Flow Height = [1.19 1.32 1.31 1.12 1.0 ...... ] Artifical Neural Networks; Iterations Input 1.32 Hidden We divided our data into two parts; -Training -Model Validation Output Hint: Process starts with initial weights, transfer function, and feed back

16. Data: Daily Domain Hint: Low correlation

17. Model Performance Table ANN SWAT ANN-SWAT Comparison based on Exp-IV forecast Model Architecture Table Hint: Modified ANN structure got the peak flows better than SWAT Remarkable Success Results: Daily Domain The results for: one day ahead streamflow prediction by only 1 previous streamflow

18. Data: Monthly Domain

19. 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) Results: Monthly Domain  ARMA (1,1)

20. 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 Results: Monthly Domain 

21. Focus of this talk: Hypothesis 2 • H2: Cluster analysis can be used successfully in evaluating flow forecasting models. TRUE or FALSE?

22. Hint: In our knowledge; this is the first application of clustering in model evaluation Simple idea behind H2;

23. We seek the same pattern group before and after simulation. It must be robust to pass the criteria. Before (Observed values) After (Predicted values) Cluster Analysis

24. A contrast but this confirmed that only K-means criteria is successfull in model evaluation. Results: Clustering for validation

25. Overall: Better performed models

26. Finally: Implementation of UMN Mapserver for Ria Formosa In this thesis, the necessary steps for constructing an end-to-end streamflow 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. Our dream: end-to-end streamflow forecasting system

27. Conclusions • The ANN flow forecasting scheme applied in Alportel River seems to have reached encouraging results, particularly in the model for hourly height values. • 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 Algarve basin or as well as other catchments in Iberian peninsula • Overall performance comparison, the criteria of MSE shows that SWAT model produced more accurate results than our ANN model but the lack of magnitudes (peak values) was a significant issue in extreme events like flood studies • The two hypoteses were achieved in this study; (Do you still remember?) >>ANN can be more accurate in flow forecasting than other models >>CA can be used for model validation

28. Fruitful 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.

29. Selected References • Liao, S.H., and Wen, C.H., 2007: Artificial Neural Networks Classification andClustering Of Methodologies and Applications – Literature Analysis from 1995 to 2005. Expert Systems with Applications (32) 1–11. • 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. • McCulloch, W. S. and Pitts, W., 1943: A Logical Calculus of the Ideas Imminent in Nervous Activity. Bulletin of Mathematical Biophysics, 5:115-133.

30. Thank you, • Funding: 1) The program ERASMUS and to himself Gerrit Gerritzon(Desiderius Erasmus) 2) Istanbul Technical University • My mentors: Dr. Flávio Martins (UALG) and Dr. Arthur J. Mariano (UM) • My Committee: Drs. Ángel Del Valls Casillas, Alice Newton, Gilliam Glegg, FranciscoLópez Aguayo and Carmen Sarasquete My Friends and Family!