Assessment of soil classification using soft computing approaches for Erenler (Afyonkarahisar) region
Yazarlar (4)
Arş. Gör. Sami Serkan Işoğlu Afyon Kocatepe Üniversitesi, Türkiye
Prof. Dr. Ahmet Yildiz Afyon Kocatepe Üniversitesi, Türkiye
Prof. Dr. Mahmut Mutlutürk Süleyman Demirel Üniversitesi, Türkiye
Dr. Öğr. Üyesi Enes CENGİZ Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Earth Science Informatics (Q1) | ||
| Dergi ISSN | 1865-0473 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 09-12-2024 |
| Cilt / Sayı / Sayfa | 18 / 1 / 1–21 | DOI | 10.1007/s12145-024-01603-0 |
| Makale Linki | https://doi.org/10.1007/s12145-024-01603-0 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| The Casagrande chart is traditionally used for determining soil classes. However, processing samples individually on this chart is time-consuming, and human error, particularly at the classification boundaries, can lead to incorrect soil classification. To address these issues, this study employs machine learning algorithms to classify different soil types more efficiently and accurately. The primary goal is to integrate machine learning into engineering geology studies, leveraging technological advancements. As part of the study, field and experimental work was conducted, beginning with the collection of 272 soil samples from the designated study area to represent the entire region. The initial physical properties of these samples were then determined. The soil samples were carefully double-bagged and transported to the laboratory to prevent any degradation. Upon arrival, the water content of the samples was … |
| Anahtar Kelimeler |
| Afyonkarahisar | Decision tree | Machine learning | Soil classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |