ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS - PowerPoint PPT Presentation

Loading...

PPT – ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS PowerPoint presentation | free to download - id: 6a2ef6-ZTIwN



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS

Description:

ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS 7th International Conference on Hydroinformatics HIC 2006, Nice, France Paul Conrads USGS South Carolina ... – PowerPoint PPT presentation

Number of Views:14
Avg rating:3.0/5.0
Slides: 16
Provided by: pconrads
Learn more at: http://sofia.usgs.gov
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: ESTIMATING WATER DEPTHS USING ARTIFICIAL NEURAL NETWORKS


1
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
2
Outline
  • Description of Study area
  • Problem
  • Model Approach
  • Model Results
  • Summary and Discussion

3
Study Area
Everglades - River of Grass
  • Pre-1940s Wide, shallow, sheet flow
  • Post-1940s System compartmentalized
  • Large Conservations Areas of shallow (lt 1 m) and
    empounded water
  • Restoration of the Everglades return the large
    ecosystem back of a river of grass

4
Quick History of the Everglades
5
Study Area (continued)
  • Large wetland system
  • Depth lt 1 m
  • Hydrology critical for defining habitat
  • Difficult gauging environment
  • Access by airboat or helicopter

Water Conservation Area 3a
6
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

7
Data Set
  • Water-level and water-depth data from WCA 3a
  • EDEN grid and vegetation attributes
  • prairie
  • sawgrass
  • slough
  • upland
  • UTM North
  • UTM South

8
Approach
  • Two stage ANN model
  • First stage estimate mean water-depths using
    static model
  • Second stage estimate water-depths variability
    using dynamic variables

9
Two-stage Model
10
Static Model Results
  • Each step represents a different site
  • Model able to generalize water level difference
    but not the variability

11
Dynamic Model
  • 5 index stations (red dots)
  • Combination of static and dynamic data
  • 5 validation stations (green dots)

12
Final Model Results
13
More Model Results
Static variables are most sensitive in the model
Model statistics for validation sites
14
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

15
Questions
Paul Conrads USGS-South Carolina Water Science
Center pconrads_at_usgs.gov Ed Roehl Advanced Data
Mining, LLP ed_at_advdatamining.com
About PowerShow.com