fuzzy multiple criteria evaluation of conservation buffer placement
DESCRIPTION
69th SWCS International Annual Conference July 27-30, 2014 Lombard, ILTRANSCRIPT
Fuzzy Multiple Criteria Evaluation of Conservation Buffer Placement Strategies in Landscapes
Zeyuan Qiu, New Jersey Institute of Technology
Jin Zou, Kunming University of Science and Technology
Yang Kang, Columbia University
July 29, 2014
Backgrounds
Conservation buffer restoration is among the best management practices for repairing impaired streams and restoring ecosystem functions in degraded watersheds.
There are different buffer placement strategies: Fixed-width riparian buffers: many existing
riparian protection rules and ordinances. Variable width riparian buffers (Phillips, 1989;
Xiang, 199; Herron and Hairsine, 1998; Basnyat et al., 1999)
Critical source areas (Bren, 2000; Tomer et al., 2003; Doskey et al., 2006; Qiu, 2003 and 2009)
Multiple criteria evaluation (Qiu et al., 2010)
Four Evaluation Criteria (Qiu, 2010) Hydrological Sensitivity measured by a
topographic index (TI) Benefits for controlling runoff generation
Soil Erodibility measured by NRCS soil erodibility index (Wischmeier and Smith, 1978)
Benefits for reducing soil erosion Wildlife Habitat measured by the potential
presence of the species of concern as evaluated by the New Jersey Landscape Project. Benefits for enhancing wildlife habitats
Impervious Surface measured by the percentage of impervious surface rate Benefits for mitigating stormwater impacts
TRKLSEI
Multiple Criteria Evaluation Framework (Qiu, 2010) Following the simple additive utility function,
the total weighted score for strategy j
where i represents the evaluation criteria and j the buffer strategies, wi the criterion weights and aijstandardized criteria values
cij is the strategy j’s class score for criterion i and reflects the combined impacts of the criterion weights and the criteria values (wiaij)
Using the average criterion class scores of the prioritized agricultural lands
I
iij
I
iijij cawC
11
Research Problems
It is very critical to assign the proper criterion weights that represent the decision makers’ preferences;
Using the class score to represent the combined impacts of the criterion weights and values as done in Qiu (2010) is overly simplified;
Deterministic way of eliciting the decision makers’ preferences may not be consistent with the nature of vagueness of decision preferences.
Research Objectives
To implement a fuzzy analytic hierarchy process (AHP) to illicit the multiple criterion weights that reflect the vague nature of the decision makers’ preferences for placing conservation buffer in agricultural lands in Raritan River Basin in New Jersey.
Evaluation Criterion Classification:Hydrological Sensitivity
Evaluation Criterion Classification:Soil Erodibility
Evaluation Criterion Classification:Wildlife Habitat
Evaluation Criterion Classification:Impervious Surface
Fuzzy Analytical Hierarchy Process (AHP)
An AHP survey was conducted in December 2012 to the participants of the NJ SWCS Chapter Fall Meeting to pairwise compare the decision criteria in a scale of nine linguistic terms;
Fifteen questionnaires were distributed and 15 were returned. Thirteen correspondents indicated that they are familiar or very familiar with conservation buffers and their benefits;
In addition to the standard comparison, we also ask the respondent to rank his or her level of confidence in giving such comparison statement in a Likert scale of 1-5, which corresponds to five linguistic label;
In the end, the respondent was also asked to rank the overall level of confidence in giving all comparison statements in the 1-5 Likert scale.
Sample of Survey Questions
To control soil erosion is _____________ than to reduce surface runoff and runoff-related pollutants. Absolutely more important Much more important Somewhat more important More important Equally important Less important Somewhat less important Much less important Absolutely less important Please rank your level of confidence in giving the statement above at 1-5 scale: ______
1 2 3 4 5
Confident Very Confident
AbsolutelyConfident
SomewhatConfident
NotConfident
Summary of the Survey ResultsQuestions Average Min. Max.
1a. To control soil erosion is ___ than to reduce surface runoff and runoff-related pollutants 4.27 4 6
1b. Please rank your level of confidence in giving the statement 4.15 3 5
2a. To control soil erosion is ___ than to enhance wildlife habitat 5.53 4 8
2b. Please rank your level of confidence in giving the statement 3.69 3 5
3a. To control soil erosion is ___ than to mitigate storm water impacts 4.60 2 8
3b. Please rank your level of confidence in giving the statement 3.85 3 5
4a. To reduce surface runoff and runoff-related pollutants is ___ than to enhance wildlife habitat 5.40 3 8
4b. Please rank your level of confidence in giving the statement 3.69 2 5
5a. To reduce surface runoff and runoff-related pollutants is ___ than to mitigate storm waterimpacts
4.80 4 7
5b. Please rank your level of confidence in giving the statement 3.92 3 5
6a. To enhance wildlife habitat is ___ than to mitigate storm water impacts 3.00 0 5
6b. Please rank your level of confidence in giving the statement 3.69 3 5
7. Please rank your overall level of confidence in giving all statements 3.87 3 5
Borda Count for Fuzzy and Linguistic Decision Making
The Borda count was originally developed as a single-winner election method in which voters rank candidates in order of preference and gradually evolved to be an appropriate procedure for group decision making to select the most preferred alternative when facing multiple decision alternatives.
The linguistic labels can be represented through trapezoid fuzzy numbers (TFNs), which can capture the vagueness of such linguistic assessments
Semantics With Nine Linguistic Labels For Comparing Decision Criterion
Label Meaning TFN V(t)
l0 Absolutely less important (0, 0, 0, 0) 0.0000
l 1 Much less important (0, 0.02, 0.05, 0.11) 0.0417
l 2 Somewhat less important (0.05, 0.11, 0.17, 0.25) 0.1433
l 3 Less important (0.17, 0.25, 0.34, 0.44) 0.2983
l 4 Equally important (0.34, 0.44, 0.56, 0.66) 0.5000
l 5 More important (0.56, 0.66, 0.75, 0.83) 0.7017
l 6 Somewhat more important (0.75, 0.83, 0.89, 0.95) 0.8567
l 7 Much more important (0.89, 0.95, 0.98, 1) 0.9583
l 8 Absolutely more important (1, 1, 1, 1) 1.0000
Semantics with Five Linguistic Labels for Ranking Confidence Level
Label Meaning TFN V(t)
d1 Not confident (0, 0.1, 0.2, 0.3) 0.15
d 2 Somewhat confident (0.2, 0.3, 0.4, 0.5) 0.35
d 3 Confident (0.4, 0.5, 0.7, 0.8) 0.60
d 4 Very confident (0.7, 0.8, 0.9, 1) 0.85
d 5 Absolutely confident (1, 1, 1, 1) 1.00
Broad Borda Counts
n
j
kij
kijik rcxr
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kikki xrpxr
1
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Where is i, j are the index for the decision criteria and k is the index for the decision makers
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kijik rxr
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Garcia-Lapresta et al. (2009)
Derived Criterion Weights
Control Soil
Erodibility
Reduce Runoff and
Runoff-related
Pollutants
Enhance Wildlife Habitat
Mitigate Stormwater
Impacts
Original Borda Count 0.199 0.212 0.331 0.258Considering the confidence in ranking the pairs
0.201 0.214 0.323 0.261
Considering the overall confidence
0.196 0.212 0.326 0.265
Comparison of Three MCDM Scenarios
MCDM-C: using the sum of the criterion classes as proposed originally by Qiu (2010)
MCDM-O: using the original data with the derived weights.
MCDM-R: using the revised data with the derived weights.
Comparison of the Areas Ranked by Any Two Scenarios (%)
MCDM-C MCDM-O MCDM-R MCDM-C MCDM-O MCDM-R
Top 5% Top 10%
MCDM-C
MDCM-O 25.89 43.67
MCDM-R 40.25 55.18 49.05 60.80
Top 15% Top 20%
MCDM-C
MDCM-O 59.14 65.84
MCDM-R 52.17 43.70 49.18 35.12
Kappa Values for Comparing Any Two Scenarios
Top 5% Top 10% Top 15% Top 20%
MCDM_Cvs
MCDM_O 0.256 0.432 0.586 0.652
MCDM_C vs
MCDM_R 0.400 0.486 0.515 0.482
MCDM_O vs
MCDM_R 0.550 0.635 0.430 0.340
Conclusion
Fuzzy MCDM didn’t appear to be superior to the simple classification-based MCDM approach. There are tradeoffs among all scientifically defensible MCDM alternative approaches MCDM-C loses the originality of the raw data MCDM-O introduces the bias of the raw data MCDM-R is not superior in all cases
All MCDM approaches give compatible targeted areas for buffer restoration and placement with the Kappa Value ranging from 0.4 to 0.7.
Always use not fancy, but understandable approach
Acknowledgement
USDA Natural Resources Conservation Services CCPI (Cooperative Conservation Partnership Initiative) program
USDA Forest Service National Agroforestry Center