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

Outline Introduction System Architecture System Demonstration Future Work Summarization

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

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

Outline Introduction System Architecture System Demonstration Future Work Summarization

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

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

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

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

Outline Introduction System Architecture System Demonstration Future Work Summarization

System Demonstration: GUI

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

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

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

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

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

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

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

Outline Introduction System Architecture System Demonstration Future Work Summarization

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

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

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

Any comment is appreciated. Thank you ! Any comment is appreciated. ykou@cs.vt.edu