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• W4294132562 abstract "This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0–0.5 m and 0.4–0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R < 0.5) were produced by the multiple linear regression." @default.
• W4294132562 created "2022-09-02" @default.
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• W4294132562 date "2022-09-01" @default.
• W4294132562 modified "2023-09-25" @default.
• W4294132562 title "Evaluation of Multiple Linear Regression and Machine Learning Approaches to Predict Soil Compaction and Shear Stress Based on Electrical Parameters" @default.
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• W4294132562 doi "https://doi.org/10.3390/app12178791" @default.
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