I IHR SEMINAR (DECEMBER 3 , 2010) EVAN ROZ

Hydroinformatics: Data Mining in Hydrology

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

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)

Deltares

Vegetation Induced Resistance (Keijer et al. 2005)

Genetic programming identifies a more concise relationship between vegetation and resistance

1DV model versus GP

Equations of the 1DV model

Equation derived from genetic programming

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

Maureen Coat-Tipping Bucket Interpolation

Inverse Distance Weight

Liska’s Method Polygone of ThiessenMost Effective:

Kriging

Teschl (2007)

• Feed forward neural network trained with reflectivity data at four altitudes above rain gauge

• Objective: Estimate precipitation at tipping bucket.

IPWRSM Inspired Future Work

Combine:

1. Radar reflectivity data from Davenport, IA (KDVN)

2. Interpolated precipitation data via Kriging of tipping buckets

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.