Estimating water depths using artificial neural networks
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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

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Estimating water depths using artificial neural networks

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
Outline

  • Description of Study area

  • Problem

  • Model Approach

  • Model Results

  • Summary and Discussion


Study area
Study Area

Everglades - River of Grass

  • 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”


Quick history of the everglades

~1940’s

~2010?

Quick History of the Everglades


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 Conservation Area 3a


Problem how to estimate water depths at ungauged sites
Problem : How to Estimate Water Depths at 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 set
Data Set

  • Water-level and water-depth data from WCA 3a

  • EDEN grid and vegetation attributes

    • % prairie

    • % sawgrass

    • % slough

    • % upland

    • UTM North

    • UTM 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



Static model results
Static Model Results

  • Each “step” represents a different site

  • Model able to generalize water level difference but not the variability


Dynamic model
Dynamic Model

  • 5 “index” stations (red dots)

  • Combination of static and dynamic data

  • 5 validation stations (green dots)



More model results
More Model Results

Static variables are most sensitive in the model

Model statistics for validation sites


Summary
Summary

  • 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


Questions
Questions

Paul Conrads

USGS-South Carolina Water Science Center

[email protected]

Ed Roehl

Advanced Data Mining, LLP

[email protected]


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