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Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]