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Journal of soil science and plant nutrition

versión On-line ISSN 0718-9516

Resumen

HOSSEINI, Mehdi; MOVAHEDI NAEINI, Seyed Ali Reza; DEHGHANI, Amir Ahmad  y  ZERAATPISHEH, Mojtaba. Modeling of soil mechanical resistance using intelligent methods. J. Soil Sci. Plant Nutr. [online]. 2018, vol.18, n.4, pp.939-951. ISSN 0718-9516.  http://dx.doi.org/10.4067/S0718-95162018005002702.

In recent years, novel techniques such as intelligent techniques are being employed for developing predictive models to estimate parameters that are difficult to measure. For instance, determining soil mechanical resistance is difficult, particularly in fine-textured soils and during warm seasons. In this research, we used statistical algorithms, adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy inference systems (FIS) in order to predict soil mechanical resistance and compared them with traditional statistical models such as multiple regression (MR). To achieve this goal, bulk density, volumetric soil water content (as predictors) and soil mechanical resistance (as target variable) were used at 0-25 cm depth with sample size equals 200. The results showed that intelligent methods are appropriate tools for minimizing the uncertainties in soil engineering projects. The ANFIS model predicted soil mechanical resistance more accurately than the other models with R2 = 0.93 and RMSE= 299.41. Also, the use of intelligent methods not only provided new approaches and methodologies to estimate soil mechanical resistance, but also minimized the potential inconsistency of correlations.

Palabras clave : Farm management; root growth; soil mechanical modeling; soil properties.

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