1. A Web-based Visualization and Analysis System for Watershed Management
Yufeng Kou, Chang-Tien Lu
Dept. of Computer Science
Virginia Tech
Thomas Grizzard, Adil Godrej, Harold Post Dept. of Civil Engineering Virginia Tech

2. Outline Introduction System Architecture System Demonstration
Future Work
Summarization

3. Introduction: Objective of the system
Build a comprehensive database of water information in the Occoquan Basin
Surface water, Ground water, Water quality
Report impact of extreme weather incidents on Occoquan water system
Flooding, draught, storm

4. Objective of the system
Water quality surveillance and evaluation
Chemical pollutant density
High efficiency data operation and dissemination
Real time data collection
Internet-based online information publication

5. Outline Introduction System Architecture System Demonstration
Future Work
Summarization

6. Hardware Architecture
Real-time Data collecting System
Connect monitoring stations to central database via telephone line
Data collecting: every 15 minutes
Web-based Information Publication
Maintain duplicate databases:
Master database: collect data from monitoring stations
Slave database: A copy of master database
Web server

7. Software Architecture
Standard 3-tier System
Thin client
All the computation and maintenance are on server side
High performance
Efficient for data centric tasks

8. Software Architecture
Automatic Data Synchronization
Master database  Slave database
By a FTP client programmed with Java
Transfer action is triggered periodically by “Scheduled Task” in Windows 2003
Only the increment of data is transmitted
Set firewall to ensure security

9. Software Architecture
Database system
Visual Foxpro 7.0
Water data database: flow, stage, …
Station database: location, description, …
User database: name, password, contact information, …
Development Tools
ASP, HTML, Javascript
Java Applet, Java Servlet, Javascript

10. Outline Introduction System Architecture System Demonstration
Future Work
Summarization

11. System Demonstration: GUI

12. System Demonstration: Data comparison between stations
Located upstream of Bull Run
Peak flow detected at 7AM, Jan 14, 2004
ST45:
Located downstream of Bull Run
Peak flow detected at 5AM, Jan 14, 2004
9 Hour gap
Useful for flood prediction

13. System Demonstration: Linear scale vs Log scale
Suitable for data with small variance
Details lost when plotting data with large variance
Log Scale:
Suitable for data with large variance
Details retained for the value between the maximum and the minimum
Not good for data with small variance

14. System Demonstration: Statistics from historical data
50 year flow data
Minimum flow
Maximum flow
Average flow
Help find interesting patterns
1980 is a dry year
1973 is a rainy year

15. System Demonstration: Data Cube
Generate the union of a set of alpha-numeric summary tables corresponding to a given hierarchy
Provide an aggregation view for different dimensions
Usually aggregate temporal property to different granularities
Or aggregate temporal dimension with spatial dimension

16. Data Cube: Stations vs Day of Month
Water flow data for 7 stations in April, May, and June 2004
Show the flow fluctuation of multiple stations in a single figure
High flow values are identified, for example
By ST01 on April 13th ,14th;
By ST01 and ST10 on May 8th and 9th
By ST01 and ST10 on June 18th and 19th

17. Data Cube: Time of Day vs Day of Month
Water flow data for ST70 in April, May, and June 2004
Clearly show the flow fluctuation at a specific time on a specific day
High flow values are identified
20PM-24PM, on April 12th;
0-3AM and 19-24PM, on April 13th
0AM-12AM, on April 14

18. Data Cube: Years vs Day of Year
Water 53 year flow data for ST70
High flow values are identified
Near the 150th day of year

19. Outline Introduction System Architecture System Demonstration
Future Work
Summarization

20. More visualization methods
Future Work
More visualization methods
3-D representation
Both spatial attribute and non-spatial attributes
Animation
Graphic tools for data comparison
Histogram, bar chart, pie chart, 2-D and 3-D colormap
Apply Data Mining Techniques
Frequent Pattern Detection
Discover the area flooded frequently in the past 30 years
Abnormal Pattern Detection
Find a week in which flow changes dramatically compared with flow in the immediate adjacent weeks

21. Future Work Apply Data Mining Techniques
Similarity Search
Find two similar pollutant leak accidents according to their impacts on the Occoquan water quality
Association Rule Formulation
Explore the relationship among temperature, humidity, and stage fluctuation
Build a decision support system
Combine GIS, Meteorological, Transportation, Economics, and Water monitoring data
Generate a comprehensive model
Rule-based system, Neural network, or Decision Tree
Predict damage of the incoming calamity and provide corresponding decision support

22. Summarization Propose a web-based visualization and analysis system
Has been successfully used for watershed management
Based on a 3-tier client/server architecture
Near real-time data collection and dissemination
Support multiple visualization methods
Table, figure, and data cube
Support download data as text file, PDF, or JPEG
Future direction
Add more visualization methods
Add data mining functionalities

23. Any comment is appreciated.
Thank you !
Any comment is appreciated.