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Developing a Graphical User Interface
to Improve Learning of Stochastic
Theory for Water Resources in the
Faisal Hossain, Jonathan Schwenk and David Huddleston
Department of Civil and Environmental Engineering
Tennessee Technological University
What is Stochastic Theory?
• Probability Theory
• Stochastic Processes
• Random Variables – Random Processes
(that can be described by a probability
• Each time the process ‘acts’ it yields a
different realization in time and space
• Repeatability is ‘probabilistic’
Why Stochastic Theory for Water
Resources Engineering?
1. Uncertainty is omni-present in natural or man-made water
resources systems.
2. Need understanding of Random functions, Probability,
Distributions, Time series, Spatial trends – to model/predict
the variability.
3. Hydrologic models – inherently uncertain (uncertain data,
model assumptions, scale issues).
4. ABET requires some concepts of Probability and Statistics
as part of CE curriculum.
Nothing stays the same FOREVER in Water Resources
Examples of Application of Stochastic
Theory in Water Resources
1. Reservoir systems analysis – forecasting upstream inflow,
power demand, navigation (optimization)
2. Flood Forecasting; Rainfall forecasting (time series
3. Data assimilation in adaptive schemes for real-time
decision making (kalman filtering)
4. Improving model structure through reduction of uncertainty
5. Spatial Interpolation of groundwater contamination
6. Flood frequency analysis (extreme value distribution)
Emerging Needs related to Stochastic
Theory in Water Resources
• More and more research conducted at
graduate level involving stochastic
theory applications
• Bloom’s learning level of entering
graduate students should be
‘understanding’ or ‘application’
• Major demand raised on admission
criterion for graduate applicants
• Graduate students should be prepared
a priori on the application of stochastic
Questions we should ask
1. Are we doing a good job with instruction of stochastic
theory in CE/Water resources?
2. What do statisticians think?
3. Are entering graduate students adequately prepared to
conduct research involving this stochastic aspect in water
4. What could we do to improve learning of students in
5. Could computer assisted schemes help? (e.g. GUI tools)
Instruction of Stochastic Theory
(What do Statisticians Think?)
“For too long we in the statistics profession have tolerated
poor statistics teaching, which produces courses that are
often rated as the worst course or the most useless course
that graduates in other fields claim they have ever taken.
We too often teach what appears to the students a
collection of unrelated methods illustrated by examples
taken from coin-tossing, card-playing and dice-rolling. And
then we expect the students to be able to translate this
wide variety of methods with simple gambling examples to
complex industrial problems involving the application of a
large number of methods".
Godfrey, B. 1986. Future Directions in Statistics. Report 10 Center for
Quality and Productivity Improvement, University of Madison, WI, 34-39.
• 1. Gauge the current state of instruction of Stochastic
Theory in Civil Engineering curriculum (survey courses).
• 2. Proof of Concept of a GUI-based instruction tool for
teaching stochastic theory in the classroom.
Stochastic Theory in Civil Engineering
Survey conducted using the world wide web only.
Survey method – search for ‘keywords’ from course title and
Keywords – ‘Stochastic’, ‘Probability’, ‘Numerical’, ‘Systems’ etc.
Information posted by university course catalog or
instructor’s website on the world wide web is accurate
and up to date.
All relevant course content information is available from
the world wide web.
All courses are actively offered on a routine basis by
The course has a significant amount of stochastic theory
component (or a nearest relative discipline) delivered as
course content.
Stochastic Theory in Civil Engineering
Total Number of Universities Surveyed
Number of Universities with www listing of relevant courses
Total number of courses identified (having the generic terms
‘stochastic’, ‘statistics’, ‘numerical’ etc in CE curricula)
% Graduate(Dual listed) and Undergraduate
Number of schools with integrated courses on Stochastic Theory
Number of courses on Stochastic Theory
Number of courses on Stochastic Theory in Water Resources and
Environmental Engineering
27 (11.2%)
Number of courses on Stochastic Theory in Water Resources only
23 (9.2%)
% is calculated by dividing the absolute number by the total number of courses surveyed (i.e. 241).
Preliminary Synopsis on Survey of Curricula
on Stochastic Theory in the Nation
Current overwhelming representation of graduate
courses perhaps underscores a current need to
rethink strategies and strive for a more equitable
distribution that would facilitate a smoother
learning experience. For example, creating more
undergraduate variants of these graduate
courses and offering them early in a student’s
CE education experience are likely to further
strengthen the appreciation of the concepts on
stochastic methods by the CE student.
Popularize Stochastic Theory using Graphical
User Interface (GUI) and Active Learning
1. A picture is worth a thousand words - Confucius
2. A picture is worth a million words if you can rapidly visualize
the ‘words’ – Anonymous
3. GUIs can rapidly visualize ‘any way’ desired – Ideal for
active learning
4. GUIs give full interactive control to manipulate and alter
concepts and see the effect graphically almost immediately
5. A lot of GUIs in mathematics education – none exists (to the
best of our knowledge) for stochastic theory in water
resources education
STEVE – Stochastic Theory Education
through Visualization Environment
Proof-of-Concept – Can it work in a classroom environment?
Core program is a
Stochastic Model – Two
Dimensional Satellite
Rainfall Error Model –
SREM2D corrupts true
rainfall using various
concepts of Stochastic
Theory to simulate
satellite rainfall
STEVE – Stochastic Theory Education
through Visualization Environment
Entity Dependence
Diagram for STEVE GUI
Screen Shot of
Coded in Java Native Interfacing – No O/S and compiler requirement!
Using GUI to improve student learning
One example – Geostatistics, Correlation lengths, spatial
1. Teach the theory – concept of variograms, lag distance,
spatial correlation, modeling variograms, correlation lengths,
interpolation (say kriging)
2. Next, allow students to use STEVE GUI:
• Alter parameters on variogram model type (exponential)
• Alter correlation lengths (high, low, medium)
• Observe – the effect on rainfall visualization
• Pose questions – seek answers – reconcile actual observation
with expected observation through theory
• Improve learning through trial and error (rapid visualization is key
to multiple iteration)
Using GUI to improve student learning
High Correlation Length
Medium Correlation
Low Correlation Length
Using GUI to improve student learning
High Probability of
Medium Probability of
Low Probability of
1. Survey of current curriculum on Stochastic Theory in
Civil Engineering reveals a dominance of graduate
courses (84%)
2. GUI for rapid visualization of stochastic theory
concepts to ‘pictures’ has merit in improving student
3. GUI educational tools for Stochastic Theory in Water
Resources engineering is absent.
4. Technical and Software issues on GUI development
need to be addressed.
Future Directions
1. Improve GUI’s computational aspect – visualization, portability, software
2. Survey ( of instructors on demand for such
instructional tool.
3. Prototype testing in summer research camps using a group of control and
test students
This work was funded by the Department of Civil and Environmental
Engineering’s DMF Project
Support received from Dr. Ambareen Siraj on the development of STEVE
GUI is greatly appreciated.
Derek Parsons of Computer Science Department led the development of