Journal of Water and Soil https://jsw.um.ac.ir Research Article Vol. 38, No. 2, May-June 2024, p. 269-283 Modeling Soil Penetration Resistance Using Regression, Artificial Neural Network and Gene Expression Programming Sh. Asghari 1* , M. Hasanpour Kashani 2 , H. Shahab Arkhazloo 3 1 and 3- Professor and Associate Professor, Department of Soil Sciences and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran, respectively. (*- Corresponding Author Email: shasghari@uma.ac.ir) 2- Assistant Professor, Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran Received: 12-02-2024 Revised: 05-03-2024 Accepted: 19-03-2024 Available Online: 19-03-2024 How to cite this article: 1 Asghari, Sh., Hasanpour Kashani, M., & Shahab Arkhazloo, H. (2024). Modeling soil penetration resistance using regression, artificial neural network and gene expression programming. Journal of Water and Soil, 38(2), 269-283. (In Persian with English abstract). https://doi.org/ 10.22067/JSW.2024.86792.1385 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 (R 2 ), 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 (dg) and geometric standard deviation (σg) 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 ©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. https://doi.org/ 10.22067/JSW.2024.86792.1385