A Comprehensive and Extensive Review of the Process of Water Infiltration in Soil
Abstract
Infiltration is the downward movement of water in the soil due to rain, surface runoff, and irrigation. It is a fundamental component of the water cycle as it has a direct influence on water use efficiencies and loss estimation. The current paper aims to investigate the process of infiltration and the forces behind the process, the mathematical and numerical models that were implemented by scientists to consequently estimate the infiltration rate and the amount of infiltration. Furthermore, the most common artificial intelligence methods used to model the process will also be explored. The findings from this review have established that the soil physical properties play a crucial role in soil infiltration behavior, as an increase in sand content and porosity is directly related to the high infiltration level while an increase in clay content, bulk density or initial water content is associated with the decrease in the infiltration rates. It was also reported that the Kostiakov model of all its forms and Philip and Horton’s were the most common and other commonly used models in the description of the infiltration process. The Hydrus-1D computational model was identified with high accuracy in regards to the modelling of the water flow, as the values of the soil parameters are to be determined with high accuracy. On the other hand, using the data produced by the researches, machine learning methods such as ANN, GEP, M5P, RF, and PINN could have the potential to provide a higher degree of accuracy in modelling the infiltration process in comparison to the classical models in terms of their predictive ability, due to their ability to fit nonlinear data and the nonlinearity of the inherent soil. Such methods can be implemented to solve the above-mentioned challenges of field measurements and the problem of the physical parameters estimation, especially in precision farming and water resource management.
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