Modeling Soil Penetration Resistance Using Regression, Artificial Neural Network and Gene Expression Programming

Document Type : Research Article

Authors

• Sh. Asghari ¹
• M. Hasanpour Kashani ²
• H. Shahab Arkhazloo ¹

¹ Department of Soil Sciences and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

² Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

10.22067/jsw.2024.86792.1385

Abstract

Introduction
The penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and expansion of roots in the soil will be significantly limited. The direct measurement of PR is also a laborious and costly task due to instrumental errors. Therefore, it is useful the use of different models such as multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) to estimate PR through easily accessible and low-cost soil characteristics. The objectives of this research were: (1) to obtain MLR, ANN and GEP models for estimating PR from the easily accessible soil variables in forest, range and cultivated lands of Fandoghloo region of Ardabil province, (2) to compare the accuracy of the aforementioned models in estimating soil PR using the coefficient of determination (R²), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria.
 
Materials and Methods
Disturbed and undisturbed samples (n = 80) were nearly systematically taken from 0-10 cm soil depth with nearly 50 m distance in forest (n = 20), range (n = 23) and cultivated (n = 37) lands of Fandoghloo region of Ardabil province, Iran (lat. 38° 24' 10" to 38° 24' 25" N, long. 48° 32' 45" to 48° 33' 5" E) in summer 2023. The contents of sand, silt, clay, CaCO₃, pH, EC, bulk (BD) and particle density (PD), organic carbon (OC), gravimetric field water content (FWC), mean weight diameter (MWD) and geometric mean diameter (GMD) were measured in the laboratory. Relative bulk density (BDrel) was calculated using BD and clay data. Mean geometric diameter (d_g) and geometric standard deviation (σ_g) of soil particles were computed by sand, silt and clay percentages. The penetration resistance (PR) of the soil was measured in situ using cone penetrometer (analog model) at 5 replicates. Data randomly were divided in two series as 60 data for training and 20 data for testing of models. The SPSS 22 software with stepwise method, MATLAB and Gene Xpro Tools 4.0 software were used to derive multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) models, respectively. A feed forward three-layer (2, 5 and 6 neurons in hidden layer) perceptron network and the tangent sigmoid transfer function were used for the ANN modeling. A set of optimal parameters were chosen before developing a best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, as they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. The accuracy of MLR, ANN and GEP models in estimating PR were evaluated by coefficient of determination (R²), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics.
 
Results and Discussion
 The studied soils had clay loam (n = 11), sandy clay loam (n = 6), sandy loam (n = 12), loam (n = 13), silty clay loam (n = 14), silty clay (n = 1) and silt loam (n = 23) textural classes. The values of sand (13.14 to 64.79 %), silt (21.11 to 74.96 %), clay (2.95 to 42.18 %), OC (1.01 to 7.17 %), FWC (11.58 to 50.47 mass percent), BD (0.84 to 1.43 g cm^-3) and PR (1.03 to 5.83 MPa) showed good variations in the soils of the studied region. There were found significant correlations between PR with FWC (r = - 0.45^**), silt (r = - 0.36^**) and σ_g (r = 0.36^**). Due to the multicollinearity of silt with σ_g (r = -0.84^**), the σ_g was not used as an input variable to estimate PR. Generally, 3 MLR, ANN and GEP models were constructed to estimate PR from measured readily available soil variables. The results of MLR, ANN and GEP models showed that the most suitable variables to estimate PR were FWC, silt and BDrel. The values of R², RMSE, ME and NS criteria were obtained equal 0.44, 1.19 MPa, 0.19 MPa and 0.36, and 0.92, 0.41 MPa, -0.05 MPa and 0.92,  0.79, 0.91 MPa, 0.13 MPa, 0.63 for the best MLR, ANN and GEP models, respectively. The former researchers also reported that there is a negative and significant correlation between PR with FWC.
 
Conclusion
 The results indicated that field water content (FWC), silt and relative bulk density (BDrel) were the most important and readily available soil variables to estimate penetration resistance (PR) in the studied area. According to the lowest values of RMSE and the highest values of NS, the accuracy of ANN models to predict soil PR was higher than MLR and GEP models in this research.    
 

Keywords

• Estimation
• Fandoghloo region
• Gravitational water content
• Readily variables
• Soil resistance

Main Subjects

• Soil science

---

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1- Asghari, Sh., & Shahabi M. (2019). Spatial variability of soil saturated hydraulic conductivity and penetration resistance in salt-affected lands around Lake Urmia. Water and Soil, 33(1), 103-116. (In Persian with English abstract). https://doi.org/10.22067/JSW.V33I1.74411

2- Asghari, Sh., Sheykhzadeh, G.R., & Shahabi, M. (2017). Geostatistical analysis of soil mechanical properties in Ardabil plain of Iran. Archives of Agronomy and Soil Science, 63(12), 1631-1643. https://doi.org/10.1080/03650340 .2017.1296136

3- Azadmard, B., Mosaddeghi, M.R., Ayoubi, S., Chavoshi, E., & Raoof, M. (2019). Estimation of near-saturated soil hydraulic properties using hybrid genetic algorithm-artificial neural network. Ecohydrology & Hydrobiology, 20(3), 437–449. https://doi.org/10.1016/j.ecohyd.2019.09.001

4- Ahmadzadeh Kaleibar, F., & Fuladipanah, M. (2023). Assessment of regression, support vector machine, and gene expression programming transfer functions to predict soil humidity parameters in Arasbaran plain. Journal of Water and Soil Science, 27(2), 135-149. https://doi.org/10.47176/jwss.27.2.42532

5- Ahmadi, A., Palizvan zand, P., & Palivan zand, H. (2018). Estimation of saturated hydraulic conductivity by using gene expression programming and ridge regression (A case study in East Azerbaijan province). Iranian Journal of Soil and Water Research, 48(5), 1087-1095. (In Persian with English abstract). https://doi.org/10.22059/ijswr. 2018.218413.667555

 6- Amirabedi, H., Asghari, Sh., Mesri, T., & Balandeh, N. (2016). Prediction of mean weight diameter of aggregates using artificial neural network and regression models. Applied Soil Research, 4(1), 39-53. (In Persian with English abstract)  

 7- Bayat, H., Neyshabouri, M.R., & Hajabbasi, M. (2008). Comparing neural networks, linear and nonlinear regression techniques to model penetration resistance. Turkish Journal of Agriculture and Forestry, 32, 425-433.

8- Blake, G.R., & Hartge, K.H. (1986a). Bulk density. p. 363-375. In: Klute A. (ed). Methods of Soil Analysis Part 1, Physical and Mineralogical Methods. 2nd ed. American Society of Agronomy, Madison, WI.

9- Blake, G.R., & Hartge, K.H. (1986b). Particle density. p. 377-381. In: Klute A. (ed). Methods of Soil Analysis Part 1, Physical and Mineralogical Methods. 2nd ed. ASA and SSSA, Madison, WI.

10- Campbell. G.S. (1985). Soil Physics with Basic: Transport Models for Soil–Plant System. Elsevier. New York. 150 p.

11- Dinarvand, H., Asghari, Sh., & Shahabi, M. (2018). Estimating soil penetration resistance using neurofuzzy, support vector machine and gene expression programming methods. Available at  https://civilica.com/doc/808626 (In Persian with English abstract)

12- Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13, 87–129.

13- Ghorbani, M.A., Deo, R.C., Kashani, M.H., Shahabi M., & Ghorbani, S. (2019). Artificial intelligence-based fast and efficient hybrid approach for spatial modeling of soil electrical conductivity. Soil and Tillage Research, 186, 152–164. https://doi.org/10.1016/j.still.2018.09.012

14- Gardner, W.H. (1986). Water content. p. 493-544. In: Klute A. (ed). Methods of Soil Analysis. Part 1. 2nd ed. Agronomy. Monograph. 9. ASA, Madison, WI.

15- Gee, G.W., & Or, D. (2002). Particle-size analysis. p. 255–293. In: Dane J. H., & Topp G. C. (eds.). Methods of Soil Analysis. Part 4. SSSA Book Series No. 5. Soil Science Society of America, Madison, WI.

16- Kelishadi, H., Mossaddeghi, M.R., Hajabbasi, M.A., & Ayoubi, S. (2014). Near-saturated soil hydraulic properties as influenced by land use management systems in Koohrang region of central Zagros, Iran. Geoderma, 213, 426-434. https://doi.org/10.1016/j.geoderma.2013.08.008

17- Kozak, E., Pachepsky, Y.A., Sokolowski, S., Sokolowska, Z., & Stepniewski, W. (1996). A modified number-based method for estimating fragmentation fractal dimensions of soils. Soil Science Society of America Journal, 60, 1291-1297.

18- Lowery, B., & Morrison, JE. (2002). Soil penetrometer and penetrability. In: Dane J.H., & Topp GC (eds.). Methods of soil analysis, part 4. Physical methods. Madison (WI): Soil Science Society of America; pp. 363–388.

19- Nelson, D.W., & Sommers, L.E. (1982). Total carbon, organic carbon, and organic matter. p. 539–579. In A.L. Page et al. (ed.) Methods of Soil Analysis. Part 2. 2^nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI.

20- Page, A.L. (ed.).(1985). Methods of Soil Analysis. Part 2. Chemical and Microbiological Methods. Agronomy No. 9. American Society of Agronomy, Madison, WI.

21- Santos, F.L., De Jesus, V.A.M., & Valente, D.S.M. (2012). Modeling of soil penetration resistance using statistical analyses and artificial neural networks. Acta Scientiarum. Agronomy, 34, 219-224.

22- Sheykhzadeh, G.R., Asghari, Sh., & Mesri Gundoshmian, T. (2016). Estimating penetration resistance in agricultural soils of Ardabil plain using artificial neural network and regression methods. Water and Soil, 30(3), 941-954. (In Persian with English abstract). https://doi.org/10.22067/JSW.V30I3.42235

23- Vaz, C.M.P., Manieri, J.M., de Maria, I.C., & Tuller, M. (2011). Modeling and correction of soil penetration resistance for varying soil water content. Geoderma, 166, 92-101. https://doi.org/10.1016/j.geoderma.2011.07.016

24- Whalley, W., To, J., Kay, B.D., & Whitmore, A.P. (2007). Prediction of the penetrometer resistance of soils with models with few parameters. Geoderma, 137, 370–377. https://doi.org/10.1016/j.geoderma.2006.08.029Get rights and content

25- Yazdani, A., Mosaddeghi, M.R., Khademi, H., Ayoubi, S., & Khayamim, F. (2014). Relationship between surface aggregate stability and some soil and climate properties in Isfahan province. Soil Management, 3(2), 23-31. (In Persian with English abstract)

26- Yoder, R.E. (1936). A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of American Society Agronomy, 28, 337-35.

27- Zhang, R., & Zhang, S. (2024). Coefficient of permeability prediction of soils using gene expression programming. Engineering Applications of Artificial Intelligence, 128(107504). https://doi.org/10.1016/j.engappai.2023.107504