ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS

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

pconrads@usgs.gov

Ed RoehlEd Roehl

Advanced Data Mining, LLPAdvanced Data Mining, LLP

ed@advdatamining.com