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The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data

  • 0College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, Fujian, China.
Environmental Monitoring and Assessment +

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June 4, 2024

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June 4, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new fuzzy assessment method unifies heterogeneous data for forest land suitability. This approach effectively ranks land resources using trapezoidal fuzzy numbers and Jaccard-Cosine similarity for objective indicator weighting.

Area Of Science

  • Environmental Science
  • Forestry Science
  • Data Science

Background

  • Forest land resource assessment is crucial for sustainable management.
  • Existing methods struggle with heterogeneous and multi-granularity data.
  • A unified approach is needed to handle diverse information sources.

Purpose Of The Study

  • To propose a consistent fuzzy assessment method for forest land suitability.
  • To develop techniques for unifying multi-granularity linguistic and numerical data.
  • To enable effective land resource evaluation using multi-source heterogeneous data.

Main Methods

  • Formulas for transforming real and interval data into fuzzy numbers.
  • Fuzzy quantitative transformation for multi-granularity uncertain linguistic information.
  • Jaccard-Cosine similarity for objective indicator weight determination.
  • Ranking alternatives using trapezoidal fuzzy numbers and centroid distance.

Main Results

  • Unified representation of multi-granularity linguistic assessment information.
  • Normalized formulas for trapezoidal fuzzy numbers.
  • Objective weights derived from Jaccard-Cosine similarity.
  • Effective ranking of forest land suitability alternatives.

Conclusions

  • The proposed consistent fuzzy assessment method successfully handles multi-source heterogeneous data.
  • This method provides a robust framework for forest land suitability assessment.
  • The approach enhances decision-making in land resource management.

Keywords:

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