Transcript Bayesian Network Model for Evaluation of Ecological River

Bayesian Network Model for
Evaluation of Ecological River
Construction
M. Arshad Awan
Bayesian Network

A probabilistic graphical model that
represents a set of random variables and
their conditional dependencies via a
directed graph (DAG), e. g.,
Ecology
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The study of the interactions of living
organisms with each other and with their
environment.
General River Management
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Flood Control
◦ Embanking
◦ Waterway management
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Water resource management
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Irrigation
Drinking water supply
Industrial water supply
Hydraulic power generation
New Demands in River
Management
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Environment-friendly
◦ Landscape, temperature, humidity, oxygen
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Ecological healthiness
◦ Species diversity, balance of food chain
◦ Abundant number of species
◦ Habitats for animals
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Water-friendly activity
◦ Exercise, rest, walking, picnic, fishing, learning,
observation
Ecological River Construction
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Nature-shaped river
◦ Recover the natural environments as close as
possible (shallows, swamp, tree, grass, etc.)
◦ Within the limit of flood controllability
◦ Ecological system recovery
◦ Sustainability

Supply the area for water-friendly activity
◦ Rest area, shelter, walkway, sports area
◦ Accessibility
Successful Ecological River
How to evaluate?
 Possible variables

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Sufficient water-quantity
Clean water-quality
Good landscape
Secure structure of nature-recovery
Convenient facility
Sufficient space, etc.
Research Definition

Goals

Technical tool
◦ To develop a model to evaluate the ecological
river construction
◦ To find the required/desired plan quantitatively
◦ Bayesian Network Model
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Expected effects
◦ Evaluation of existing rivers
◦ Evaluation of results on investment
◦ Provide the suggestion to reconstruct and
manage the facility
◦ Provide the guideline for the new project
Progress in term project
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Survey:
◦ Ecological river engineering
◦ Bayesian belief networks (BBN)
Selection of input variables for BBN
 Tool to develop BBN
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◦ Netica
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Development of proposed BBN
Input variables 1
 Water Quantity
- sufficient water quantity is one of the most significant factor to
characterize a river.
- but too much water in a urban river is not always good
in the aspect of flood control, safety issue, maintenance cost,
and etc.
- perceptions on how much water is sufficient are very subjective.
lack
sufficient
Too much
10 20 30 40 50 60 70 80 90 100
Input variables 2
 Water Quality
- People are very sensitive on the water quality.
- The more clean and clear, the better
- It costs a lot to maintain the desired water quality.
- The desired water quality of river is not necessarily to be high as
the quality of drinking or industrial water
- perceptions on the desired water quality of river are very subjective.
dirty
1
2
clean
3
4
Very clean
5 6 7 8 9 10
Input variables 3
 Ecology
- One of main goals of stream restoration is ecological balance and
soundness.
- It can be measured by biodiversity, the number of a species,
ecological system service, habitat areas for wild lives, and etc.
bad
1
average
2
3
4
good
5 6 7 8 9 10
Input variables 4
 Landscape
- Landscape of a river is composed of many factors
- trees, plants, forest and wetland, riparian corridor with built
environment, bank, and etc.
- perceptions on landscape are very subjective and may be
characterized by 3 linguistic terms: excellent, good, ordinary.
ordinary
1
2
3
good
4
excellent
5 6 7 8 9 10
Input variables 5
 Stream shape (Fluvial geomorphology)
- Stream shape is very important to ensure the self-purification of
water and the sustainability of ecosystem by supplying various
aquatic environments.
- Stream shape should be restored as close as possible, but must not
decrease the flood controllability.
- replacement of shore protection, islands, shoals, pools, fish-ladder,
removal of artificial facilities such as water steps and small dams,
etc.
natural
artificial
1
2
3
4
5
6
Too natural
7 8 9 10
Input variables 6
 Facility
- people want to do some activities near a river
- Although artificial facilities may not be good for the ecological
system, the least amount of facilities to provide people with
accessibility and water-friendly activities are necessary
- shelter, rest area, walkway, exercise facility, road, parking lot, etc.
- In some cases, too many facilities are constructed.
- In some cases, people ask more facilities.
- How many facilities are reasonable?
sufficient
lack
1
2
3
4
5
6
Too many
7 8 9 10
Bayesian Belief Network (BBN)
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Structure
◦ Connection of nodes (DAG)
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Inference
◦ Infer the value of variables
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Learning
◦ Training examples
Building BBN Structures
Netica (BBN Tool)
Netica (BBN Tool)
Proposed BBN
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To evaluate a river, a set of nodes are connected:
◦ based on the combination of 6 input variables
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The output of evaluation can be differentiated
based on the criteria which uses different sets
of variables
◦ comprehensive evaluation : 6 inputs
◦ aquatic environment evaluation:
 quantity, quality, ecology
◦ land environment evaluation:
 landscape, stream shape, facility
◦ Balance/successful evaluation : 6 inputs comparison
Ecological River Construction
Network report
Aquatic Environment (CPT)
Land Environment (CPT)
Ecological River Const. (CPT)
A random training sample
Learning Algorithm
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There are three main types of algorithms
that Netica uses to learn CPTs:
◦ Counting,
◦ Expectation-maximization (EM), and
◦ Gradient descent.
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Counting is:
◦ Fastest, simplest, and can be used whenever
there is not much missing data, or uncertain
findings for the learning nodes or their
parents.
References
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Woo, H., Trends in ecological river engineering in Korea, Journal of
Hydro-environment Research (2010), doi:10.1016/j.
jher.2010.06.003.
Finn V. Jensen and Thomas D. Nielsen, “Bayesian Networks and
Decision Graphs”, February 8, 2007, Springer.
Judea Pearl, “Probabilistic Reasoning in Intelligent Systems:
Networks of Plausible Inference”.
Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann.
2006. Guidelines for developing and updating Bayesian belief
networks applied to ecological modeling and conservation.
Canadian Journal of Forest Research 36:3063-3074.
McCann, R., B. G. Marcot, and R. Ellis. 2006. Bayesian belief
networks: applications in natural resource management. Canadian
Journal of Forest Research 36:3053-3062.
References
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Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. M. Rowland, and M.
J. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and
wildlife population viability under land management alternatives
from an environmental impact statement. Forest Ecology and
Management 153(1-3):29-42.
The Anticipated Impacts of the Four Rivers Project (ROK) on
Waterbirds (Birds Korea Preliminary Report).
Workshop on hydro-ecological modeling of riverine organisms and
habitats, ecological processes and functions (6th to 7th of June
2005, The Netherlands).
http://www.gleon.org/ (Global Lake Ecological Observatory
Network).
http://en.wikipedia.org/.
Sandra Lanini, “Water Management Impact Assessment Using A
Bayesian Network Model”, 7th International Conference on
Hydroinformatics, HIC 2006, Nice, FRANCE.
Thanks!