Scopus
YÖKSİS Eşleşti
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
PROFESÖR 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
PROFESÖR 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
PROFESÖR 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
ÖĞRETİM GÖREVLİSİ HAMZA NEGİŞ →
Makale Bilgileri
DergiAgricultural Water Management
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
Metrikler
51
Atıf
3
Yazar
5
Anahtar Kelime