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ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS. 7th International Conference on Hydroinformatics HIC 2006, Nice, France. Paul Conrads USGS South Carolina Water Science Center Ed Roehl Advanced Data Mining. Outline. Description of Study area Problem Model Approach Model Results - PowerPoint PPT Presentation
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U.S. Department of the InteriorU.S. Geological Survey
ESTIMATING WATER DEPTHS USING ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKSARTIFICIAL NEURAL NETWORKS
7th International Conference on Hydroinformatics7th International Conference on Hydroinformatics
HIC 2006, Nice, FranceHIC 2006, Nice, France
Paul ConradsPaul ConradsUSGS South Carolina Water Science CenterUSGS South Carolina Water Science Center
Ed RoehlEd Roehl
Advanced Data MiningAdvanced Data Mining
OutlineOutline
Description of Study area Problem Model Approach Model Results Summary and Discussion
Study AreaStudy Area
Pre-1940s: Wide, shallow, sheet flow Post-1940s: System compartmentalized Large Conservations Areas of shallow (< 1 m)
and empounded water Restoration of the Everglades – return the
large ecosystem back of a “river of grass”
Everglades - River of GrassEverglades - River of Grass
Quick History of the EvergladesQuick History of the Everglades
~1940’s~1940’s ~2010?~2010?
Study Area (continued)Study Area (continued)
Large wetland system Depth < 1 m Hydrology critical for defining
habitat Difficult gauging environment Access by airboat or helicopter
Water Water Conservation Conservation Area 3aArea 3a
Problem : How to Estimate Water Depths Problem : How to Estimate Water Depths at Ungauged Sitesat Ungauged Sites
Using a subset of Everglades domain
Available data (static and dynamic) Vegetation data Water-level and water-
depth data at 17 sites
Data SetData Set
Water-level and water-Water-level and water-depth data from WCA depth data from WCA 3a3a
EDEN grid and EDEN grid and vegetation attributesvegetation attributes % prairie% prairie % sawgrass% sawgrass % slough% slough % upland% upland UTM NorthUTM North UTM SouthUTM South
Approach Approach
Two stage ANN model
First stage – estimate mean water-depths using static model
Second stage – estimate water-depths variability using dynamic variables
Two-stage ModelTwo-stage Model
Static Model ResultsStatic Model Results
•Each “step” represents a different siteEach “step” represents a different site
•Model able to generalize water level Model able to generalize water level difference but not the variabilitydifference but not the variability
Dynamic ModelDynamic Model
5 “index” stations (red dots)
Combination of static and dynamic data
5 validation stations (green dots)
Final Model ResultsFinal Model Results
More Model ResultsMore Model Results
Static variables Static variables are most are most sensitive in the sensitive in the modelmodel
Model Model statistics for statistics for validation sitesvalidation sites
SummarySummary
Estimation of water depth at ungaged sites ANNs able to accurately predict water depths at
ungaged sites Use of static and dynamic variable produce a
multi-variate “kreiging” of water depths Methodology will be used to hindcast “new”
network stations
QuestionsQuestions
Paul ConradsPaul Conrads
USGS-South Carolina Water Science CenterUSGS-South Carolina Water Science Center
Ed RoehlEd Roehl
Advanced Data Mining, LLPAdvanced Data Mining, LLP