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 PostDept. of Civil EngineeringVirginia 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 • ST60: • 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 • Linear 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 • 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. 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. Thank you ! Any comment is appreciated. ykou@cs.vt.edu