Data-driven Zone Classification

Data-Driven Zone Classification is a zone classification method that derives zoning boundaries and categories through systematic analysis of empirical data, distinguished from rule-based or expert-driven approaches by its reliance on quantitative patterns and statistical or machine learning models to define spatially coherent zones.

5

Publications

143

Citations

20

Authors

12

Institutions

Publications per year

2017–2026

4

Authors

20

  1. BT
    Behzad Tokhmechi

    University of Shahrood, Iran

    1 publications · 23 citations

    1

    h-index

  2. VL
    Vajira Lasantha

    The University of Tokyo, Japan

    1 publications · 25 citations

    1

    h-index

  3. AB
    A. Bashari

    National Iranian Oil Company (Iran), Iran

    1 publications · 23 citations

    1

    h-index

  4. AM
    Amna M. Handhal

    University of Basrah, Iraq

    1 publications · 21 citations

    1

    h-index

  5. MA
    Majid Ansari Jafari

    University of Tehran, Iran

    1 publications · 23 citations

    1

    h-index

1–5 of 20

Venues

  1. Remote Sensing

    1 publications · 57 citations

    1

    h-index

  2. Journal of Geophysics and Engineering

    1 publications · 23 citations

    1

    h-index

  3. Journal of Petroleum Science and Engineering

    1 publications · 21 citations

    1

    h-index

  4. Advances in Meteorology

    1 publications · 25 citations

    1

    h-index

  5. Scientific Data

    1 publications · 17 citations

    1

    h-index

1–5