Creating semi-continuous pedotransfer functions for estimating soil water retention curve using the M5 tree method

Document Type : Complete scientific research article

Authors

1 PhD student, Department of Soil Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

2 Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

Abstract

Background and Objectives: Recently, pseudo-continuous transfer functions (PC-PTFs) have been introduced for estimating soil water retention curve (SWRC). The M5 tree method is similar to regression trees, where linear functions are located in its leaves, and it has a high capability in creating transfer functions. These functions are highly sensitive to the power of machine learning algorithms. However, so far, the powerful M5 tree method has not been used to develop PC-PTFs for a wide range of soil textures. Additionally, the effectiveness of some soil structural variables in improving PC-PTFs has not been investigated, so far. Furthermore, the dependency of the error distribution of PC-PTFs on soil textural triangles to various factors has not been deeply examined. Therefore, the objectives of this study were to develop PC-PTFs using the M5 method, investigate the effect of soil structural variables on the performance of these functions, and examine the error dependence of these functions on different factors.
Materials and Methods: A total of 120 soil samples were collected from depths of 10 to 60 centimeters, with agricultural, orchard, and pastureland uses of Tehran and Hamedan provinces, and soil texture, bulk density (BD), SWRC, saturated hydraulic conductivity (Ks), organic matter (OM), mean weight diameter (MWD) of soil aggregates, and penetration resistance at 300 hectopascals (PR300) were measured. Thirteen PC-PTFs, in three groups of inputs, were developed to estimate SWRC, using M5 tree and non-linear regression methods. The error distribution of all PC-PTFs was plotted on the soil texture triangle, according to root mean square error (RMSE).
Results:
In the first function, soil suction was used as the only estimator. A non-linear regression model produced an acceptable model for the first function with a R2 of 0.718. In PC-PTFs 3 to 6, components of soil texture, BD, and FC (at 300 hPa matric suction) and PWP (at 15000 hPa matric suction) moisture contents were used to estimate SWRC. The R2for these functions ranged from 0.719 to 0.990, indicating an improvement in the performance of SWRC estimation. In the M5 method, the use of FC significantly improved the model performance and created an optimal model, resulting in RMSE of 0.015 and 0.020 cm³cm⁻³, and R² of 0.987 and 0.973 in the training and validation stages, respectively. In the M5 method, any function using Ks and MWD as estimators showed significant improvement compared to PC-PTF4, which used soil texture components and BD as estimators. The AIC values in both training and validation stages in the M5 method were 37% to 283% and 111% to 157% lower compared to non-linear regression, respectively. The error distribution on the soil texture triangle showed no dependence on soil texture but was related to the method of creating PC-PTFs and relevant input variables.
Conclusions: A powerful artificial intelligence methods can be employed to create a comprehensive model for SWRC. This would eliminate the need for users to rely on various SWRC models such as van Genuchten for different soils. Incorporating a set of soil texture and structure variables increases the accuracy of SWRC estimation. However, among structural variables, those indicating pore size distribution were more suitable for SWRC estimation. The greater impact of FC compared to PWP demonstrated the higher efficiency of moisture in intermediate matric suctions for SWRC estimation. The robust algorithm of the M5 tree method identified some patterns of relationships between input and output variables that were not detectable by non-linear regression. Considering the dependence of error distribution on the soil texture triangle to the method of creating PC-PTFs and input variables, categorizing error distribution maps should be done based on the mentioned factors.

Keywords

Main Subjects

References

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Journal of Soil Management and Sustainable Production
Volume 14, Issue 3
October 2024
Pages 53-76

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