69th SWCS International Annual Conference July 27-30, 2014 Lombard, IL

1. 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

2. 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)

3. 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
T
RKLSEI 

4. 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 aij
standardized 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
i
ij
I
i
ijij cawC
11

5. 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.

6. 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.

7. Evaluation Criterion Classification:
Hydrological Sensitivity

8. Evaluation Criterion Classification:
Soil Erodibility

9. Evaluation Criterion Classification:
Wildlife Habitat

10. Evaluation Criterion Classification:
Impervious Surface

11. 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.

12. 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
Absolutely
Confident
Somewhat
Confident
Not
Confident

13. Summary of the Survey Results
Questions 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 water
impacts
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

14. 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

15. 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

16. 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

17. Broad Borda Counts


n
j
k
ij
k
ijik rcxr
1
)( 

m
k
ikki xrpxr
1
)()(
Where is i, j are the index for the decision criteria and k is
the index for the decision makers


n
j
k
ijik rxr
1
)( 

m
k
iki xrxr
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)()(
Garcia-Lapresta et al. (2009)

18. 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 0.258
Considering 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

19. 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.

20. 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

21. Kappa Values for Comparing Any
Two Scenarios
Top 5% Top 10% Top 15% Top 20%
MCDM_C
vs
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

22. 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

23. Acknowledgement
 USDA Natural Resources
Conservation Services CCPI
(Cooperative Conservation
Partnership Initiative) program
 USDA Forest Service National
Agroforestry Center