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🔓 Açık Erişim YÖKSİS Eşleşti
Saturated Hydraulic Conductivity Estimation Using Artificial Intelligence Techniques: A Case Study for Calcareous Alluvial Soils in a Semi-Arid Region
Water (Switzerland) · Aralık 2022
YÖKSİS Kayıtları
Saturated Hydraulic Conductivity Estimation Using Artificial Intelligence Techniques: A Case Study for Calcareous Alluvial Soils in a Semi-Arid Region
WATER · 2022 SCI-Expanded
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Saturated Hydraulic Conductivity Estimation Using Artificial Intelligence Techniques: A Case Study for Calcareous Alluvial Soils in a Semi-Arid Region
MDPI AG · 2022 SCI-Expanded
PROFESÖR CEVDET ŞEKER →
Makale Bilgileri
DergiWater (Switzerland)
Yayın TarihiAralık 2022
Cilt / Sayfa14
Scopus ID2-s2.0-85143718481
Erişim🔓 Açık Erişim
Özet
The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory measurement to present adequately soil properties in an area of interest. Moreover, the estimation process is labor and time-intensive due to the difficulties of collecting the soil samples from the field. Hence, innovative methods, such as machine learning techniques, can be an alternative to estimate Ks. This might facilitate agricultural water and nutrient management which has an impact on food and water security. In this spirit, the study presents neural-network-based models (artificial neural network (ANN), deep learning (DL)), tree-based (decision tree (DT), and random forest (RF)) to estimate Ks using eight combinations of soil data under calcareous alluvial soils in a semi-arid region. The combinations consisted of soil data such as clay, silt, sand, porosity, effective porosity, field capacity, permanent wilting point, bulk density, and organic carbon contents. The results compared with the well-established model showed that all the models had satisfactory results for the estimation of Ks, where ANN7 with soil inputs of sand, silt, clay, permanent wilting point, field capacity, and bulk density values showed the best performance with mean absolute error (MAE) of 2.401 mm h−1, root means square error (RMSE) of 3.096 mm h−1, coefficient of determination (R2) of 0.940, and correlation coefficient (CC) of 0.970. Therefore, the ANN could be suggested among the neural-network-based models. Otherwise, RF could also be used for the estimation of Ks among the tree-based models.
Yazarlar (7)
1
Sevim Seda Yamaç
2
Hamza Negiş
3
Cevdet Şeker
4
Azhar M. Memon
ORCID: 0000-0003-0982-2265
5
Bedri Kurtuluş
6
Mladen Todorovic
ORCID: 0000-0002-8911-8755
7
Gadir Alomair
ORCID: 0000-0001-8557-3796
Anahtar Kelimeler
artificial neural network
decision tree
deep learning
random forest
soil conductivity
soil data
Kurumlar
Istituto Agronomico Mediterraneo di Bari
Valenzano Italy
King Fahd University of Petroleum and Minerals
Dhahran Saudi Arabia
King Faisal University
Al-Ahsa Saudi Arabia
Konya Gida ve Tarim Üniversitesi
Konya Turkey
Muğla Sıtkı Koçman Üniversitesi
Mugla Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
4
Atıf
7
Yazar
6
Anahtar Kelime