Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightThe results also showed that a linear a and a constant a with a maximum coefficient of determination and minimum error are the best scaling parameters to estimate the SWRC.
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Özet
The direct measurement of the soil water retention curve (SWRC) in a laboratory is time-consuming, difficult, and costly. Thus, many attempts have been made to predict the water retention curve indirectly from the physical and chemical properties of soil. The particle size distribution curve is one of the indirect methods used to predict the water retention curve. The Arya and Paris (AP) model predicts the soil water retention curve from soil particle size distribution (PSD) data. The AP model estimates pore radius from the radius of spherical particles by using a scaling parameter (a). The objective of this study was to investigate the effect of predicting the scaling parameter with different methods to improve estimation of the SWRC. The evaluation of methods was done on 35 soil samples with different textures from the eastern region of Guilan Province in Iran. The results showed that estimated curves with different a values gave different results that depended highly on the scaling parameter. Therefore, a determination has a key role in estimating the soil water retention curve. The results also showed that a linear a and a constant a with a maximum coefficient of determination and minimum error are the best scaling parameters to estimate the SWRC.
Abstract
The direct measurement of the soil water retention curve (SWRC) in a laboratory is time-consuming, difficult, and costly. Thus, many attempts have been made to predict the water retention curve indirectly from the physical and chemical properties of soil. The particle size distribution curve is one of the indirect methods used to predict the water retention curve. The Arya and Paris (AP) model predicts the soil water retention curve from soil particle size distribution (PSD) data. The AP model estimates pore radius from the radius of spherical particles by using a scaling parameter (a). The objective of this study was to investigate the effect of predicting the scaling parameter with different methods to improve estimation of the SWRC. The evaluation of methods was done on 35 soil samples with different textures from the eastern region of Guilan Province in Iran. The results showed that estimated curves with different a values gave different results that depended highly on the scaling parameter. Therefore, a determination has a key role in estimating the soil water retention curve. The results also showed that a linear a and a constant a with a maximum coefficient of determination and minimum error are the best scaling parameters to estimate the SWRC.