Predicting Water Quality in Northwest Indiana Team members: Carl Summers, Zhe Wei Wang, Brian Hunter, Joseph Robertson Project Mentor: Dr. Ruijian Zhang Purdue University Calumet

Purdue University Calumet Undergraduate Research Achievements  Research extended to the IEEE CHC 61 Web Programming Competition  Received funding through Purdue University Research Department to pursue See5.0 Web implementation  Collaborating with Indiana’s Department of Environmental Management

Outline of Presentation:   Water Quality Prediction Motivation Preparing Data Output of See5 decision tree  Website Data Graphical RepresentationData Graphical Representation Web TechnologiesWeb Technologies Flash Professional 8Flash Professional 8 Cascading Style SheetsCascading Style Sheets ASP.NET Framework 2.0ASP.NET Framework 2.0

Purdue University Calumet Undergraduate Research I. Water Quality Prediction

  Current mechanistic models require significant expert input to provide accurate forecasts.   These systems are typically used to predict trends in water quality over a vast region and long timelines.   Improving the detail of a mechanistic model may be too difficult, costly, or time consuming. Traditional Mechanistic Models

Modeling Methods Artificial Intelligence Data Mining Bayesian Statistics Decision Tree See5 Traditional Mechanistic Models Implement and compare Decision Trees, Bayesian Networks, and the traditional Mechanistic modeling techniques.

See5 – A Decision Tree Tool   See5 generates a text file containing a rule-set, used for classifying (predicting) each record in a data-set, into a discrete set of pre-determined classifications ({Good, Bad}, {Above, Normal, Below}, etc.).   Utilizes information gain, from information theory, to determine which attributes to “split” the data on.

Data Set   Raw data was sparse   Many attributes were useless   Required extensive work to glean useful information.   Not classified

Clustering Unclassified data from USGS Clustering Process Classified Data See5 requires classified input data. Clustering is composed of two parts: 1)A function to group together similar points, and ultimately similar clusters. We refer to these functions as a whole as Joining Methods. 2)A function to quantify the similarity between points or clusters. These are referred to as Similarity Metrics.

Attribute 1 Attribute 2 Clustering

DatePrecipitationSuspended SedimentDissolved OxygenFlow RateTemperatureClassification 12/15/ Good 12/22/ Bad 12/29/ Good 1/5/ Bad 1/12/ Bad 1/19/ Good 1/26/ Bad 2/2/ Bad 2/9/ Good 2/16/ Good 2/23/ Good 3/2/ Bad 3/9/ Bad 3/16/ Good 3/23/ Good Clustered Data Set

Offset Classification DatePrecipitation Suspended Sediment Flow Rate Temperature 12/15/ /22/ /29/ /5/ /12/ /19/ Classification Good Bad Good Bad Good

Decision Tree DatePrecipitation Suspended Sediment Dissolved Oxygen Flow RateTemperatureClassification 12/15/ Good 12/22/ Bad 05/23/ ???

Purdue University Calumet Undergraduate Research II. See5.0 Web Solution

Purdue University Calumet Undergraduate Research Objective  Share a visualization of the predictions generated by See5 with the public.  To provide viewers with a user interface to easily display descriptive and complex data in a comprehensive environment.

Purdue University Calumet Undergraduate Research Methods  To provide a cross-platform interface by conforming to W3C Standards Web languages will function through various Web browsers Web languages will function through various Web browsers Provides consistency to define the appearance of an entire Web site Provides consistency to define the appearance of an entire Web site  Take advantage of Web technologies No package installation required from the user No package installation required from the user Always available (per server uptime) Always available (per server uptime) User interaction User interaction  Easy to deploy and manage

Website

Interactive Content Page

Purdue University Calumet Undergraduate Research Data Graphical Representation  Applying various languages to supply a fully scalable application to the user Flash 8 Professional will provide rich animation and an elegant user interface Flash 8 Professional will provide rich animation and an elegant user interface CSS will allow consistency of format throughout the site CSS will allow consistency of format throughout the site ASP.NET 2.0 allows embedded Flash objects ASP.NET 2.0 allows embedded Flash objects Returns server-side code and code-behind files into plain HTML Returns server-side code and code-behind files into plain HTML

Purdue University Calumet Undergraduate Research Flash Professional 8  Many users won’t be able to install arbitrary ActiveX controls or use a Java plug-in, whereas Flash is preinstalled with Windows on corporate machines, even most Linux distributions come pre-packaged with Flash  Flash can consume raw XML data to draw real-time graphs to easily determine water quality  Advantages of ActionScript 2.0 Object Oriented Programming Language Object Oriented Programming Language Permits vector based objects to be manipulated quickly and easily, on-the-fly! Permits vector based objects to be manipulated quickly and easily, on-the-fly!

Purdue University Calumet Undergraduate Research Cascading Style Sheets  Allows the provision of a standardized layout throughout the site Modulation Modulation End result with CSS means cleaner code End result with CSS means cleaner code  Provides the user with a consistent interface Conventional throughout the entire page Conventional throughout the entire page  CSS allows updating to become an easy task Modifications on one style sheet can affect some or all pages, which are linked to that style Modifications on one style sheet can affect some or all pages, which are linked to that style

Purdue University Calumet Undergraduate Research ASP.NET Framework 2.0  Have accessibility to the.NET Framework 2.0 Class Library  Easy deployment, configuration, and management with IIS 6 (Windows Server 2003) XML Metabase Schema provides quick deployment XML Metabase Schema provides quick deployment Easy to use GUI management utility (inetmgr) Easy to use GUI management utility (inetmgr) Quick to update latest security patches Quick to update latest security patches  Security Authentication to lock out users without proper credentials to administrate or view the content of the page

Purdue University Calumet Undergraduate Research Summary  Using clustering tools to classify data in preparation for See5  Using See5 to generate a rule set  Use the rule set to obtain predictions  Ultimately implement and compare other prediction methods  Provide a public website for the visualization of the prediction