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Full-Text Articles in Forest Sciences
Comparing Neural Networks, Linear And Nonlinear Regression Techniques To Model Penetration Resistance, Hossein Bayat, Mohammad Reza Neyshaburi, Mohammad Ali Hajabbasi, Ali Akbar Mahboubi, Mohammad Reza Mosaddeghi
Comparing Neural Networks, Linear And Nonlinear Regression Techniques To Model Penetration Resistance, Hossein Bayat, Mohammad Reza Neyshaburi, Mohammad Ali Hajabbasi, Ali Akbar Mahboubi, Mohammad Reza Mosaddeghi
Turkish Journal of Agriculture and Forestry
Penetration resistance (PR) is an important property of soils, and can be expressed as cone index (CI). Because of high variability, there are no accurate and representative PR data in most cases. Variable PR is considerably affected by gravimetric soil water content (GWC) and bulk density (BD). In this study, artificial neural networks (ANNs) were used to simulate relationship between BD, GWC, and CI. A data set of 381 samples was collected from 2 study sites, Hamadan and Maragheh. Pedotransfer functions (PTFs) were developed using ANNs and linear and nonlinear regression models to predict CI for the combined data set …