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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

Bayesian Network Model for Evaluation of Ecological River Construction

M. ArshadAwan

Bayesian network

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

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

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

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

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

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

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

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

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

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

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

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

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

Bayesian Belief Network (BBN)

  • Structure

    • Connection of nodes (DAG)

  • Inference

    • Infer the value of variables

  • Learning

    • Training examples

Building bbn structures

Building BBN Structures

Netica bbn tool

Netica (BBN Tool)

Netica bbn tool1

Netica (BBN Tool)

Proposed bbn

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 construction1

Ecological River Construction

Network report

Network report

Aquatic environment cpt

Aquatic Environment (CPT)

Land environment cpt

Land Environment (CPT)

Ecological river const cpt

Ecological River Const. (CPT)

A random training sample

A random training sample

Learning algorithm

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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