Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
Agricultural Water Management · Mayıs 2020
YÖKSİS Kayıtları
Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
Agricultural Water Management · 2020 SCI
Prof. Dr. CEVDET ŞEKER →
Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
AGRICULTURAL WATER MANAGEMENT · 2020 SCI-Expanded
Prof. Dr. CEVDET ŞEKER →
Evaluation of machine learning methods to predict soil moisture constants
with different combinations of soil input data for calcareous soils in a semi
arid area
Agricultural Water Management · 2020 SCI
Prof. Dr. CEVDET ŞEKER →
Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
AGRICULTURAL WATER MANAGEMENT · 2020 SCI-Expanded
Öğr. Gör. HAMZA NEGİŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Effects of different emitter space and water stress on yield and quality of processing tomato under semi arid climate conditions
2020 ISSN: 03783774 SCI-Expanded 5 atıf
Prof. Dr. AYNUR ÖZBAHÇE →
Effects of irrigation interval and quantity on the yield and quality of confectionary pumpkin grown under field conditions
2015 ISSN: 03783774 SCI-Expanded
Prof. Dr. DURAN YAVUZ →
Effects of irrigation interval and quantity on the yield and quality of confectionary pumpkin grown under field conditions
2015 ISSN: 0378-3774 SCI-Expanded
Doç. Dr. NURCAN YAVUZ →
Effects of irrigation interval and quantity on the yield and quality of confectionary pumpkin grown under field conditions
2015 ISSN: 0378-3774 SCI-Expanded 1 atıf
Doç. Dr. MUSA SEYMEN →
Effects of irrigation interval and quantity on the yield and quality of confectionary pumpkin grown under field conditions
2015 ISSN: 03783774 SCI-Expanded 1 atıf
Prof. Dr. ÖNDER TÜRKMEN →
Performance of partial root-zone drip irrigation for sugar beet production in a semi-arid area
2016 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. ERCAN CEYHAN →
Performance of partial root zone drip irrigation for sugar beet production in a semi arid area
2016 ISSN: 03783774 SCI
Prof. Dr. RAMAZAN TOPAK →
Performance of partial root-zone drip irrigation for sugar beet production in a semi-arid area
2016 ISSN: 03783774 SCI-Expanded
Prof. Dr. BİLAL ACAR →
Identification of drought-tolerant pumpkin (Cucurbita pepo L.) genotypes associated with certain fruit characteristics, seed yield, and quality
2019 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. DURAN YAVUZ →
Identification of drought-tolerant pumpkin (Cucurbita pepo L.) genotypes associated with certain fruit characteristics, seed yield, and quality
2019 ISSN: 0378-3774 SCI
Prof. Dr. ÖNDER TÜRKMEN →
Identification of drought-tolerant pumpkin (Cucurbita pepo L.) genotypes associated with certain fruit characteristics, seed yield, and quality
2019 ISSN: 0378-3774 SCI-Expanded
Doç. Dr. MUSA SEYMEN →
Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
2020 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. CEVDET ŞEKER →
How do rootstocks of citron watermelon (Citrullus lanatus var. citroides) affect the yield and quality of watermelon under deficit irrigation?
2020 ISSN: 0378-3774 SCI-Expanded
Prof. Dr. ERTAN SAİT KURTAR →
How do rootstocks of citron watermelon (Citrullus lanatus var. citroides) affect the yield and quality of watermelon under deficit irrigation?
2020 ISSN: 0378-3774 SCI-Expanded
Prof. Dr. DURAN YAVUZ →
How do rootstocks of citron watermelon (Citrullus lanatus var. citroides) affect the yield and quality of watermelon under deficit irrigation?
2020 ISSN: 0378-3774 SCI-Expanded
Doç. Dr. MUSA SEYMEN →
Effects of water stress applied at various phenological stages on yield, quality, and water use efficiency of melon
2021 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. HACER ÇOKLAR →
Structure Stability of Cultivated Soils from Semi-Arid Region: Comparing the Effects of Land Use and Anionic Polyacrylamide Application
2020 ISSN: 0378-3774 SSCI Q1
Prof. Dr. CEVDET ŞEKER →
Effects of water stress applied at various phenological stages on yield, quality, and water use efficiency of melon
2021 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. DURAN YAVUZ →
How do rootstocks of citron watermelon (Citrullus lanatus var. citroides) affect the yield and quality of watermelon under deficit irrigation?
2020 ISSN: 0378-3774 SCI-Expanded Q1
Dr. Öğr. Üyesi SİNAN SÜHERİ →
Agronomic and physio-biochemical responses of lettuce to exogenous sodium nitroprusside (SNP) applied under different irrigation regimes
2023 ISSN: 0378-3774 SCI-Expanded Q1
Prof. Dr. DURAN YAVUZ →
Makale Bilgileri
ISSN03783774
Yayın TarihiMayıs 2020
Cilt / Sayfa234
Scopus ID2-s2.0-85081129396
Özet
This study evaluated the performance of deep learning (DL), artificial neural network (ANN) and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting point (PWP) using four combinations of soil data. The DL, ANN and kNN models are compared with the previous published pedotransfer functions (PTF). The data consist of 256 calcareous soil samples collected from Konya-Çumra plain, Turkey. The results demonstrated that the DL_a with inputs of soil texture components, bulk density, organic matter and lime contents, particle density and aggregate stability showed the best performances with coefficient of determination (R2) of 0.829, correlation coefficient (r) of 0.911, mean absolute error (MAE) of 0.027 and relative root mean square error (RRMSE) 9.397 % in FC estimation for calcareous soil samples. For the PWP estimation of calcareous soil samples, the kNN_b with soil texture components, bulk density, organic matter and lime content and particle density indicated the best performance with the value of R2 to 0.800, of r to 0.894, of MAE to 0.021 and RRMSE to 12.043 %. Lastly, the results showed that the DL, ANN and the kNN models perform better than the previously applied PTF for calcareous soils. Therefore, the DL model could be recommended for the estimation of FC when full soil data are available and the kNN model could be recommended for estimation of PWP with all combinations of soil data.
Yazarlar (3)
1
Sevim Seda Yamaç
2
Cevdet Şeker
3
Hamza Negiş
Anahtar Kelimeler
Artificial neural network
Deep learning
Field capacity
k-nearest neighbour
Permanent wilting point
Kurumlar
Konya Gida ve Tarim Üniversitesi
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Agricultural Water Management
Q1
OA
SJR Skoru1,736
H-Index189
YayıncıElsevier B.V.
ÜlkeNetherlands
Agronomy and Crop Science (Q1)
Earth-Surface Processes (Q1)
Soil Science (Q1)
Water Science and Technology (Q1)
Metrikler
67
Atıf
3
Yazar
5
Anahtar Kelime