Bayesian network model for evaluation of ecological river construction
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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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Bayesian Network Model for Evaluation of Ecological River Construction

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Bayesian Network Model for Evaluation of Ecological River Construction

M. ArshadAwan

Bayesian Network

  • A probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed graph (DAG), e. g.,


  • The study of the interactions of living organisms with each other and with their environment.

General River Management

  • Flood Control

    • Embanking

    • Waterway management

  • Water resource management

    • Irrigation

    • Drinking water supply

    • Industrial water supply

    • Hydraulic power generation

New Demands in River Management

  • Environment-friendly

    • Landscape, temperature, humidity, oxygen

  • Ecological healthiness

    • Species diversity, balance of food chain

    • Abundant number of species

    • Habitats for animals

  • Water-friendly activity

    • Exercise, rest, walking, picnic, fishing, learning, observation

Ecological River Construction

  • 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

    • Sufficient water-quantity

    • Clean water-quality

    • Good landscape

    • Secure structure of nature-recovery

    • Convenient facility

    • Sufficient space, etc.

Research Definition

  • Goals

    • To develop a model to evaluate the ecological river construction

    • To find the required/desired plan quantitatively

  • Technical tool

    • Bayesian Network Model

  • 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

  • Survey:

    • Ecological river engineering

    • Bayesian belief networks (BBN)

  • Selection of input variables for BBN

  • Tool to develop BBN

    • Netica

  • Development of proposed BBN

Input variables 1



Too much

10 20 30 40 50 60 70 80 90 100

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.

Input variables 2



Very clean

1 2 3 4 5 6 7 8 9 10

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.

Input variables 3




1 2 3 4 5 6 7 8 9 10


- 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.

Input variables 4




1 2 3 4 5 6 7 8 9 10


- 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.

Input variables 5

Too natural



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.

1 2 3 4 5 6 7 8 9 10

Input variables 6

Too many



1 2 3 4 5 6 7 8 9 10


- 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?

Bayesian Belief Network (BBN)

  • Structure

    • Connection of nodes (DAG)

  • Inference

    • Infer the value of variables

  • Learning

    • Training examples

Building BBN Structures

Netica (BBN Tool)

Netica (BBN Tool)

Proposed BBN

  • To evaluate a river, a set of nodes are connected:

    • based on the combination of 6 input variables

  • 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

  • There are three main types of algorithms that Neticauses to learn CPTs:

    • Counting,

    • Expectation-maximization (EM), and

    • Gradient descent.

  • 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.


  • 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.


  • 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).

  • (Global Lake Ecological Observatory Network).


  • Sandra Lanini, “Water Management Impact Assessment Using A Bayesian Network Model”, 7th International Conference on Hydroinformatics, HIC 2006, Nice, FRANCE.


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