Download Data mining in Hydrology

Hydroinformatics: Data Mining in
Hydrology
IIHR SEMINAR (DECEMBER 3, 2010)
EVAN ROZ
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.
2.
Short Term Prediction of Urban Pluvial
Floods (Maureen Coat 2010)
 Objective: Interpolate available rain
gauge data
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.
Statistics based
Physical
meteorology
𝑍 = 𝑎𝑅𝑏
Maureen Coat-Tipping Bucket Interpolation
 Inverse Distance Weight
 Liska’s Method
 Polygone of Thiessen
 Most 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.