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Hydroinformatics : Data Mining in Hydrology

Hydroinformatics : Data Mining in Hydrology . IIHR Seminar (December 3, 2010 ) Evan Roz. UNESCO-IHE, Delft, Dr. Solomatine. Hydroinformatics t echniques were adopted from computational intelligence (CI)/intelligent systems/machine learning hydroinformatics

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Hydroinformatics : Data Mining in Hydrology

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  1. Hydroinformatics: Data Mining in Hydrology IIHR Seminar (December 3, 2010) Evan Roz

  2. UNESCO-IHE, Delft, Dr. Solomatine • Hydroinformatics • techniques were adopted from computational intelligence (CI)/intelligent systems/machine learning hydroinformatics • conceptual model : data for calibration. • data-driven model: data for training/validation. • Shortcomings: • knowledge extraction • Strengths: • models quickly developed • highly accurate short term forecast • feature selection algorithms

  3. Data Mining in Hydroinformatics Rainfall-runoff modeling/Short term forecasts (Vos & Rientjes 2007) Rain-fall-runoff and groundwater model calibration-Genetic Algorithm (Franchini 1996) Flood forecasting (Yu & Chen 2005) Evapotranspiration (Kisi 2006) and infiltration estimation (Sy 2006)

  4. Deltares Vegetation Induced Resistance (Keijeret al. 2005) Genetic programming identifies a more concise relationship between vegetation and resistance

  5. 1DV model versus GP Equations of the 1DV model Equation derived from genetic programming

  6. Imperial Collegeof London Value of High Resolution Precipitation Data • Short TermPrediction of UrbanPluvial Floods(Maureen Coat 2010) • Objective: Interpolateavailablerain gauge data • Real-time Forecasting of Urban Pluvial Flooding (AngélicaAnglés 2010) • Objective: Improved analysis of the existing rainfall data obtained by both rain gauges and radar networks. Statistics based Physical meteorology

  7. Maureen Coat-Tipping Bucket Interpolation Inverse Distance Weight Liska’sMethod Polygone of Thiessen Most Effective: Kriging

  8. Teschl (2007) • Feed forward neural network trained with reflectivity data at four altitudes above rain gauge • Objective: Estimate precipitation at tipping bucket.

  9. IPWRSM Inspired Future Work Combine: Radar reflectivity data from Davenport, IA (KDVN) Interpolated precipitation data via Kriging of tipping buckets

  10. Questions? Franchini, M. and Galeati, G. (1997). “Comparing Several Genetic Algorithm Schemes for the Calibration of Conceptual Rainfall-runoff Models.” Hydrological Sciences Journal, 42, 3, 357 — 379. Keijzer, M., Baptist, M., Babovic, V., and Uthurburu, J.R. (2005). “Determining Equations for Vegetation Induced Resistance using Genetic Programming.” GECCO’05, June 25–29, 2005, Washington, DC, USA. See, L., Solomatine, D., and Abrahart, R. (2007). “Hydroinformatics: Computational Intelligence and Technological Developments in Water Science Applications.” Hydrological Sciences Journal, 52, 3, 391 — 396. Vos, N.J. and Rientjes,T.H.M. (2008). “Multiobjective Training Of Artificial Neural Networks For Rainfall-runoff Modeling.” Water Resources Research, 44, W08434.

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