Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models

Ergün Kahveci; Salih Malkoçoğlu

doi:10.17475/kastorman.2047250

EN TR

Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models

Öz

Aim of study: This study aimed to estimate the soil organic carbon (SOC) content in red pine (Pinus brutia Ten.) plantation areas under semiarid climatic conditions using multiple linear regression (MLR) and artificial neural network (ANN) models. Area of study: The research was carried out within the boundaries of the Tokat-Niksar Ayvaz Forest Management Directorate, Türkiye. Material and method: The study material consisted of 92 disturbed soil samples collected from the topsoil (0-10 cm and 10-30 cm) at 46 sampling plots, along with vegetation and topographic indices. Various physical and chemical analyses were performed on the soil samples. In addition to soil properties, topographic and remote sensing indices were used as predictor variables. SOC estimations were carried out using MLR and ANN models. Main results: According to the findings, SOC content increased with higher wilting point (WP), elevation, and normalized difference vegetation index (NDVI), while it decreased with increasing lime content (CaCO₃), pH, and bulk density (BD). In the MLR model, WP, BD, pH, CaCO₃, soil moisture index (MSI), easterly exposure (EExp.), and valley depth (VDep.) were identified as significant variables, resulting in an R² value of 0.822. The ANN model showed higher predictive performance with the same variables, yielding R² = 0.937 and a low relative prediction error (4.3%). Research highlights: Both models were found suitable for SOC estimation; however, the ANN model represented complex and nonlinear environmental interactions more effectively.

Anahtar Kelimeler

Soil Organic Carbon, ANN, MLR, Semiarid Ecosystem, Remote Sensing

Yapay Sinir Ağı ve Regresyon Modelleri Yardımıyla Toprak Organik Karbonunun Tahmini

Öz

Çalışmanın amacı: Bu çalışma, yarı kurak iklim koşullarına sahip kızılçam (Pinus brutia Ten.) ağaçlandırma alanlarında toprak organik karbon (TOK) içeriğini, çoklu doğrusal regresyon (ÇDR) ve yapay sinir ağı (YSA) modelleri kullanarak tahmin etmeyi amaçlamıştır. Çalışma alanı: Araştırma, Tokat-Niksar Ayvaz Orman İşletme Şefliği sınırları içerisinde yürütülmüştür. Materyal ve yöntem: Çalışma materyalini, 46 örnek alanda üst topraktan (0–10 cm ve 10–30 cm) alınan toplam 92 bozulmuş toprak örneği ile vejetasyon ve topoğrafik indisler oluşturmuştur. Toprak örnekleri üzerinde çeşitli fiziksel ve kimyasal analizler yapılmıştır. Toprak özelliklerine ek olarak, topoğrafik ve uzaktan algılama indisleri de tahmin değişkenleri olarak kullanılmıştır. TOK tahminleri ÇDR ve YSA modelleriyle gerçekleştirilmiştir. Temel sonuçlar: Bulgulara göre, TOK içeriği solma noktası (SN), yükselti ve normalize fark vejetasyon indeksi (NDVI) arttıkça artmış; buna karşılık kireç içeriği (CaCO₃), pH ve hacim ağırlığı (HA) arttıkça azalmıştır. ÇDR modelinde SN, HA, pH, CaCO₃, toprak nem indeksi (MSI), doğuya bakı (EExp.) ve vadi derinliği (VDep.) anlamlı değişkenler olarak belirlenmiş ve R² = 0.822 elde edilmiştir. YSA modeli ise aynı değişkenlerle daha yüksek bir tahmin performansı göstererek R² = 0.937 ve düşük göreli tahmin hatası (%4.3) değeriyle öne çıkmıştır. Araştırma vurguları: Her iki model de TOK tahmini için uygun bulunmuş; ancak YSA modeli, karmaşık ve doğrusal olmayan çevresel ilişkileri daha etkili biçimde temsil etmiştir.

Anahtar Kelimeler

Toprak Organik Karbonu, Yapay Sinir Ağı, Çoklu Doğrusal Regresyon, Yarı Kurak Ekosistem, Uzaktan Algılama

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Ormancılık (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Ergün Kahveci
Türkiye

Salih Malkoçoğlu ^*
Türkiye

Yayımlanma Tarihi

28 Eylül 2026

Gönderilme Tarihi

26 Haziran 2025

Kabul Tarihi

8 Ocak 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 26 Sayı: 2

DOI

https://doi.org/10.17475/kastorman.2047250

IZ

https://izlik.org/JA44GY92LK

APA

Kahveci, E., & Malkoçoğlu, S. (2026). Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models. Kastamonu University Journal of Forestry Faculty, 26(2), 241-260. https://doi.org/10.17475/kastorman.2047250

AMA

1.Kahveci E, Malkoçoğlu S. Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models. Kastamonu University Journal of Forestry Faculty. 2026;26(2):241-260. doi:10.17475/kastorman.2047250

Chicago

Kahveci, Ergün, ve Salih Malkoçoğlu. 2026. “Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models”. Kastamonu University Journal of Forestry Faculty 26 (2): 241-60. https://doi.org/10.17475/kastorman.2047250.

EndNote

Kahveci E, Malkoçoğlu S (01 Eylül 2026) Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models. Kastamonu University Journal of Forestry Faculty 26 2 241–260.

IEEE

[1]E. Kahveci ve S. Malkoçoğlu, “Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models”, Kastamonu University Journal of Forestry Faculty, c. 26, sy 2, ss. 241–260, Eyl. 2026, doi: 10.17475/kastorman.2047250.

ISNAD

Kahveci, Ergün - Malkoçoğlu, Salih. “Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models”. Kastamonu University Journal of Forestry Faculty 26/2 (01 Eylül 2026): 241-260. https://doi.org/10.17475/kastorman.2047250.

JAMA

1.Kahveci E, Malkoçoğlu S. Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models. Kastamonu University Journal of Forestry Faculty. 2026;26:241–260.

MLA

Kahveci, Ergün, ve Salih Malkoçoğlu. “Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models”. Kastamonu University Journal of Forestry Faculty, c. 26, sy 2, Eylül 2026, ss. 241-60, doi:10.17475/kastorman.2047250.

Vancouver

1.Ergün Kahveci, Salih Malkoçoğlu. Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models. Kastamonu University Journal of Forestry Faculty. 01 Eylül 2026;26(2):241-60. doi:10.17475/kastorman.2047250

Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models

Öz

Anahtar Kelimeler

Yapay Sinir Ağı ve Regresyon Modelleri Yardımıyla Toprak Organik Karbonunun Tahmini

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster