hydroinformatics : data mining in hydrology
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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 - PowerPoint PPT PresentationTRANSCRIPT
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I IHR SEMINAR (DECEMBER 3 , 2010) EVAN ROZ
Hydroinformatics: Data Mining in Hydrology
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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
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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)
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Deltares
Vegetation Induced Resistance (Keijer et al. 2005)
Genetic programming identifies a more concise relationship between vegetation and resistance
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1DV model versus GP
Equations of the 1DV model
Equation derived from genetic programming
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Imperial College of London
Value of High Resolution Precipitation Data
1. Short Term Prediction of Urban Pluvial Floods (Maureen Coat 2010)
Objective: Interpolate available rain gauge data
2. Real-time Forecasting of Urban Pluvial Flooding (Angélica Anglés 2010)
Objective: Improved analysis of the existing rainfall data obtained by both rain gauges and radar networks.
𝑍=𝑎 𝑅𝑏
Physical meteorology
Statistics based
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Maureen Coat-Tipping Bucket Interpolation
Inverse Distance Weight
Liska’s Method Polygone of ThiessenMost Effective:
Kriging
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Teschl (2007)
• Feed forward neural network trained with reflectivity data at four altitudes above rain gauge
• Objective: Estimate precipitation at tipping bucket.
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IPWRSM Inspired Future Work
Combine:
1. Radar reflectivity data from Davenport, IA (KDVN)
2. Interpolated precipitation data via Kriging of tipping buckets
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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.