Makale detayı · 2022
Digital mapping and spatial variability of soil quality ındex for desertification in the Akarçay Basin under the semi-arid terrestrial ecosystem using neutrosophic fuzzy-AHP approach
YÖKSİS
OpenAlex
SJR Q1
JCR Q2
Atıf 19
Üst %10
Yüzdelik 94.5%
FWCI 4.85
- Yıl
- 2022
- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- YÖKSİS dergi adı Natural Hazards
- Katalog eşleşmesi (ISSN) Natural Hazards
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
Özet henüz derlenmedi; DergiPark / OpenAlex kuyruğu işlenince burada görünecek.
Konular
Atıflar
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19 atıf
OpenAlex cited_by_count (önbellek / veritabanı)
Yerel katalogda bu makaleye atıf yapan 16 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).
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- Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region 2023
- Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region 2023
- Assessment of the neutrosophic Fuzzy-AHP and predictive power of some machine learning approaches for maize silage soil quality 2024
- Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem 2023
- Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem 2023
- Post-earthquake debris waste management with interpretive-structural-modeling and decision-making-trial, and evaluation-laboratory under neutrosophic fuzzy sets 2024
- Enhancing the soil quality index model based on neutrosophic fuzzy‐AHP integrated with remote sensing and artificial intelligence technique 2025
- Assessing the neutrosophic Fuzzy-AHP based soil quality index for sugar beet: a comparative study of multi-class logistic regression, random forest, and one-against-all support vector machine models 2026
- Assessing the neutrosophic Fuzzy-AHP based soil quality index for sugar beet: a comparative study of multi-class logistic regression, random forest, and one-against-all support vector machine models 2025