Predicting solute transfer to surface runoff using neural networks

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 samp les were taken at the end of the flume, and the concentration of potassium chloride was analyzed. The relationships between the controlled input varia bles 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 hid den nodes was observed. (C) 1998 IAWQ Published by Elsevier Science Ltd. Al l rights reserved.