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    Application of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area
    (Elsevier Sci Ltd, 2023) Tuncay, Tulay; Alaboz, Pelin; Dengiz, Orhan; Baskan, Oguz
    In the current study, the use of regression-kriging (RK), artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) methods from machine learning algorithms, were used to estimate field capacity (FC), permanent wilting point (PWP), available water content (AWC) and their performance was compared. A data set obtained from 354 surface soil samples taken randomly, mostly from agricultural areas is used. The soil data set includes pH, EC, calcium carbonate equivalent (CaCO3 equivalent), particle size distribution, and bulk density (BD) values. The results showed that while FC showed a negative strong correlation (p < 0.001) with sand (r:-0.69), BD (r:-0.85), and silt (r:-0.47), it showed a positive strong correlation (p < 0.001) with C (r: 0.90). Similarly, PWP showed a negative strong correlation with (p < 0.001) sand (r:-0.73), BD (r:0.88), and silt (r:-0.42) but a positive strong correlation (p < 0.001) with C (r: 0.90). While AWC showed a negative strong correlation (p < 0.001) with sand (r:-0.61), BD (r:-0.76), it found a positive strong correlation (p < 0.001) with FC (r: 0.97), clay (r: 0.83), and PWP (r: 0.74). In the stepwise regression results showed that particle size were prominent as the most important factor in the regression equation created for FC, PWP and AWC. Moreover, FC is the most important factor to predict AWC. For the soil FC, ANN was best with excellent accuracy (RPD = 2.71), followed by SVM (2.42), RF (2.21) while RK was poor accuracy (1.10 and 1.04). Similarly, among the machine learning algorithms (RF and SVM), ANN obtained superiority by producing lower RRMSE (7.84%), RMSE (2.83%), MAE (2.37%), MAPE (7.45%), with the largest Lin's concordance correlation coefficient (LCCC) (0.961) compared to other methods. For PWP and AWC, ANN was the best algorithm with excellent and good accuracy RPD 3.17 and 1.95 respecively. In addition, other machine learning algorithms have been the same value range in terms of LCCC. Therefore, we recommend the ANN machine-learning algorithm is more favorable to predict FC, PWP and AWC than both RK and other machine learning methods.
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    Öğe
    Computational intelligence applied to the least limiting water range to estimate soil water content using GIS and geostatistical approaches in alluvial lands*
    (Wiley, 2021) Alaboz, Pelin; Baskan, Oguz; Dengiz, Orhan
    The use of machine learning methods in pedotransfer functions has attracted considerable attention in recent years. These methods are fast and effective in solving complex events. The least limiting water range (LLWR) feature is very important in terms of water uptake by the plant and root development in agricultural production. In this study, the predictability of the LLWR feature was investigated with artificial neural networks, deep learning (DL) and the k-nearest neighbour (k-NN) algorithm from machine learning methods. Estimated values obtained from the model with the best estimation accuracy and observed values were evaluated through a geostatistical method from which their spatial distribution maps were created. In the present study, which was carried out on alluvial lands with different soil properties, the LLWR values of soils vary between 5.5% and 25.9%. Field capacity, bulk density, clay, organic matter, and lime content properties, which have a high correlation with the LLWR, were taken into consideration in the estimation methods. DL was determined as the best estimation method (mean absolute error [MAE]: 0.94%; root mean square error [RMSE]: 1.45%; coefficient of determination [R-2]: 0.93), and the worst was k-NN (MAE: 2.00%; RMSE: 2.55%; R-2: 0.77) for the LLWR. In addition, the LLWR can be estimated with high accuracy by using ReLU and softmax functions in the DL method. The study shows that distribution maps created with LLWR values obtained by observed data and the DL method have a very similar pattern.
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    Öğe
    Physico-chemical and mineralogical changes of lithic xerorthent soils on volcanic rocks under semi-arid ecological conditions
    (Univ Nacional De Colombia, 2022) Demir, Sinan; Alaboz, Pelin; Dengiz, Orhan; Senol, Huseyin; Yilmaz, Kamil; Baskan, Oguz
    This study investigates the mineralogical changes and soil development processes of young soils formed on va-rious bedrocks of volcanic origin under the same land use/land cover and climate conditions. The current study was conducted in Lithic Xerorthent soils formed on tuff, trachybasalt, and trachyandesite bedrock between San-dikli-Suhut districts of Afyonkarahisar. The three soil profiles excavated in the study area were classified in Enti-sols order based on Soil taxonomy. The primary minerals, sanidine and muscovite, and the clay minerals, smec-tite, kaolinite, and illite, were widely determined in three soil profiles which were named Profile I (PI), Profile II (PII), and Profile III (PIII). According to the chemical alteration index (CIA) values, which indicate weathering, the soils formed on the tuff bedrock were slightly weathered (77.04%). The chemical weathering index (CIW) in the soils' surface horizons formed on the trachybasalt and trachyandesite bedrock are classified as non -weathe-ring rocks with 24.43% and 33.88%. Basic cations are found at high levels in the tuff bedrock. The determination of phillipsite, gismondin and calcite minerals is an indication that the mineral content of the bedrock and the bedrocks have a significant effect on soil formation. The relationship between the bedrock and the soil has been revealed. As a result of the study, it was concluded that there were significant differences in their physico-chemical characteristics, weathering rates, and mineralogical properties. However, they were characterized as young soils since they do not contain any subsurface diagnostic horizons on the volcanic bedrock under the same climatic and land use/land cover conditions.
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    Öğe
    Yarı-kurak ekolojik koşullar altında farklı kayaç türleri üzerinde oluşmuş toprakların fiziko-kimyasal ve jeo-kimyasal özellikleri
    (2021) Alaboz, Pelin; Demir, Sinan; Senol, Hüseyın; Dengiz, Orhan; Yılmaz, Kamil; Başkan, Oğuz
    Toprağı oluşturan ana materyalin niteliği, toprak oluşumunu ve besin elementlerinin elverişliliğini önemli ölçüde etkileyen bir faktördür. Bu çalışmada; Afyon ili Sandıklı ilçesinde bulunan magmatik, metamorfik ve tortul kayaçlar üzerinde oluşmuş toprakların bazı fiziko-kimyasal özelliklerdeki değişimlerin belirlenmesi ve jeokimyasal özelliklerin karşılaştırılması amaçlanmıştır. Arazi kullanım türü mera ve kuru tarım olan toprakların fiziko-kimyasal özelliklerindeki değişkenlik en yüksek metamorfik, en düşük ise tortul kayaçlar üzerinde oluşmuş topraklarda belirlenmiştir. Tortul kayaçlar üzerinde oluşan topraklar genellikle kireç içeriği yüksek ve hafif alkalin reaksiyonlu olup, bazik katyonlarca zengindir. Tortul kayaçlarda kil ile tarla kapasitesi ve solma noktası arasında çok kuvvetli ilişki belirlenmiştir (r:0.93;0.89; p<0.001). Ayrıca, Fe içeriğinin kil (r: 0.71) ve solma noktası ile pozitif (r: 0.75) yönlü yüksek seviyeli ilişki gösterdiği de tespit edilmiştir. Magmatik kayaçlar üzerinde oluşmuş topraklarda ise Mg ile solma noktası (0.78; p<0.001) ve kum (r:-0.77; p<0.001) önemli yüksek ilişki göstermiştir. Metamorfik kayaçlarda oluşum gösteren topraklarda kum ve Fe içeriği diğer toprak özellikleri ile negatif yönlü, değişebilir katyonlar ile tarla kapasitesi ve solma noktasında ise kuvvetli, pozitif yönlü korelasyon belirlenmiştir. Magmatik kayaçlar üzerinde oluşmuş topraklarda P2O5, tortullarda CaO, metamorfiklerde ise SiO2 içerikleri diğer toprak gruplarına göre istatistiksel olarak önemli değişim sergilemiştir (p<0.01). Genel olarak temel toprak özelliklerinde arazi kullanımı ve ana materyale bağlı istatistiksel olarak önemli değişkenlikler tespit edilmiştir. Majör oksitlerde ise ana kayadaki farklılığa göre önemli, arazi kullanımına bağlı değişimler ise önemsiz bulunmuştur. Çalışma sonucunda, yarı kurak ekolojik koşulları altında toprak oluşturan faktörlerinden birisi olan ana materyalin toprak özellikleri üzerinde önemli derecede farklılıklar gösterdiği ortaya konmuştur.