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Lab 10: Suitability Mapping using Fuzzy Logic

Lab 10: Suitability Mapping using Fuzzy Logic. Define Goal – Create a Habitat Suitability Map for Nests Define Potential Factors Vegetation Water Roads Determine the Relationship Between Factors and Nests Preprocess Data. Compute Distance from Water and Roads

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Lab 10: Suitability Mapping using Fuzzy Logic

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  1. Lab 10: Suitability Mapping using Fuzzy Logic • Define Goal – Create a Habitat Suitability Map for Nests • Define Potential Factors • Vegetation • Water • Roads • Determine the Relationship Between Factors and Nests • Preprocess Data. • Compute Distance from Water and Roads • Reclass Distances Into Distance Zones??? • Convert Data to Relative Value • Fuzzy Logic Membership Function (0-1 scoring scheme) • Combine Factors • Weighted Sum • AND • OR

  2. Advantages of the Fuzzy Logic Approach • Ability to account for uncertainty • Relationships can be developed with minimum data • Relatively simple approach • Robust, handle missing data, uncertainty • Easy to interpret and communicate results • Promote and facilitate user participation

  3. Fuzzy Logic Approach In classical set theory, membership in a given set is defined as either true or false (i.e. 1 or 0). However, membership in a fuzzy set is expressed on a continuous scale from 1 (full membership) to 0 (full non-membership), so individual measurements of an environmental factor may be defined according to their degree of membership in the set between 1 and 0. Many watershed assessment problems can be viewed as ranking watersheds as either being in the “Very Good Condition” set (1) or “Very Bad Condition set (0), with most of the watershed somewhere in between to two extremes (partial membership).

  4. Landfill Site Selection

  5. f(x) f(x) Transformed Fuzzy 1 Scoring Function Fuzzy membership function Fuzzy membership function A related to an environmental factor related to an environmental factor 3 A 2 A FMS 1 0 g(x) x x 0 1 Vm Vo Vx B1 B1 B2 Potential range of values

  6. Fuzzy Membership Score: • FMSi = ƒ(Environment Factor Xi) • Weighted FMSi = [A1 + ((A3 – A1)/2)] + [A2 – ((A3 – A1)/2) – A1] • We will not be doing this step in our Lab. • Composite Fuzzy Score: • n • CFS = ∑ (Wi * FMSi) • i = 1 • Wi = weighting factor for environmental factor i

  7. Other Ways to Combine • Fuzzy OR (Maximum function) - Increasive • Fuzzy AND (Minimum function) - Decreasive • Fuzzy Product (multiply FMSs) - Decreasive • Fuzzy Sum: CFS = 1 – Π (1 – FMSi) – Increasive • Gamma: CFS = Fuzzy_sumY * Fuzzy_product1-Y

  8. Lab 9 Results Vegetation V = 0.58

  9. Roads

  10. Water V = 0.46

  11. Model Testing You should perform additional tests to assess model performance. • Chi-square and K-S can be used. • Relative indices. • If possible use an independent data set for testing.

  12. Model Strength

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