Predicting solute transfer to surface runoff using neural networks Available to Purchase

In this study, a series of experiments were performed in a laboratory flume with a medium-packed bed. Surface runoff was controlled to pass at various flow rates, velocities, and runoff depths over the medium bed. Runoff samples were taken at the end of the flume, and the concentration of potassium chloride was analyzed. The relationships between the controlled input variables and the affected output variables was modeled using artificial neural networks (ANN). Many different ANN-architectures were investigated and this work shows that an optimum architecture with minimum RMS error at five hidden nodes was observed.