Application of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area

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dc.contributor.authorTuncay, Tulay
dc.contributor.authorAlaboz, Pelin
dc.contributor.authorDengiz, Orhan
dc.contributor.authorBaskan, Oguz
dc.date.accessioned2024-12-24T19:26:59Z
dc.date.available2024-12-24T19:26:59Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractIn 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.
dc.identifier.doi10.1016/j.compag.2023.108118
dc.identifier.issn0168-1699
dc.identifier.issn1872-7107
dc.identifier.scopus2-s2.0-85166331601
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compag.2023.108118
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6443
dc.identifier.volume212
dc.identifier.wosWOS:001051097700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofComputers and Electronics in Agriculture
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectField capacity
dc.subjectPermanent wilting point
dc.subjectAvailable water content
dc.subjectRegression kriging
dc.subjectMachine learning
dc.titleApplication of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area
dc.typeArticle

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