Modeling multidimensional flow in wettable and water-repellent soils using artificial neural networks

This study examined the use of three different classes of artificial neural networks for modeling water flow in wettable and water-repellent soils, using both synthetic numerical data and experimentally measured data. The 1D self-organizing maps (SOM) successfully rendered the moisture contour in the transition zone of the wetting plumes for all soil types at different flow rates. Due to SOMs inability to generate external output data, multilayer perceptrons (MLP) and modular neural networks (MNN), respectively, were combined with SOM to predict the moisture contour for both wettable and water-repellent soils. Due to dimensionality reduction, the 1D SOM failed to capture high moisture content classes of water-repellent soils with anomalous wetting patterns, whereas spatial moment analysis succeeded in providing an accurate, albeit indirect, description. Hence, the MLP and MNN networks were applied to predict the spatial moments. The comparison between the predicted and the experimental measures demonstrated the capability of the MLP and SOM to predict the spatial moments. Comparison of the two different artificial neural networks indicated no significant difference between their results.