Análises de dados de curvas de calibrações do papel filtro (sucção) e aplicações de redes neurais artificiais na estimativa da umidade obtida pela técnica TDR em solos não saturados
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Data
2026-02-06
Autores
Santos, Nelson de Carvalho
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Universidade Federal do Espírito Santo
Resumo
Soil water content (θv) and suction are variables that influence the hydraulic and mechanical behavior of unsaturated soils (US). However, direct measurements of these quantities may be costly and operationally limited, motivating the use of indirect methods, such as Time Domain Reflectometry (TDR), for estimating θv, and the filter paper method (FPM), for estimating matric or total suction. In this context, this dissertation aimed to analyze and compare the performance of artificial neural networks (ANNs) and regression equations in the calibration of TDR for estimating soil water content, as well as to evaluate the performance of bilinear and exponential calibration curves of the FPM, based on experimental data available in the literature, in estimating matric suction in different US. For TDR, a database was compiled from the literature aiming at calibration through regression equations and ANNs, considering as input variables the apparent dielectric constant (Ka), dry bulk density (BD), organic matter content (OM), and clay content (% clay). For the FPM, bilinear and exponential calibrations reported in the literature were evaluated, considering initially air-dried filter paper and the wetting contact path. The results indicated, for TDR, superior performance of ANNs compared to regression equations, with higher coefficients of determination (R2) and lower errors, expressed by the root mean square error (RMSE) and mean absolute error (MAE), in addition to better generalization capacity when applied to external datasets. It was observed that the exclusive use of Ka is insufficient to adequately represent the variability of θv, and the best-performing architectures were those combining Ka with BD, OM, and clay content, with emphasis on ANN12-6, which presented the best overall performance and behavior close to the ideal 1:1 condition for different soil types. For the FPM, bilinear calibrations generally remained within normative tolerances; however, they showed greater variability and performance loss under high suction regimes, especially in soils with higher fine fractions. Among exponential calibrations, better overall performance was observed, with estimates predominantly within confidence intervals. Overall, it is concluded that the appropriate selection of calibration approaches and input variables is essential to reduce uncertainties in estimating θv and suction in unsaturated soils, highlighting the superior performance of ANN-based approaches and exponential FPM calibrations.
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Palavras-chave
Umidade volumétrica , Sucção matricial , Redes neurais artificiais