Bayesian Network Model for Evaluation of Ecological River Construction

1. Bayesian Network Model for Evaluation of Ecological River Construction M. ArshadAwan

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

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

4. General River Management • Flood Control • Embanking • Waterway management • Water resource management • Irrigation • Drinking water supply • Industrial water supply • Hydraulic power generation

5. 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

6. 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

7. 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.

8. 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

9. 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

10. Input variables 1 lack sufficient 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.

11. Input variables 2 dirty clean 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.

12. Input variables 3 bad average good 1 2 3 4 5 6 7 8 9 10 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.

13. Input variables 4 ordinary good excellent 1 2 3 4 5 6 7 8 9 10 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.

14. Input variables 5 Too natural natural artificial 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

15. Input variables 6 Too many sufficient lack 1 2 3 4 5 6 7 8 9 10 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?

16. Bayesian Belief Network (BBN) • Structure • Connection of nodes (DAG) • Inference • Infer the value of variables • Learning • Training examples

17. Building BBN Structures

18. Netica (BBN Tool)

19. Netica (BBN Tool)

20. 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

21. Ecological River Construction

22. Network report

23. Aquatic Environment (CPT)

24. Land Environment (CPT)

25. Ecological River Const. (CPT)

26. A random training sample

27. 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.

28. References • 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.

29. References • 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.

30. Thanks!