In this paper we describe the use of artificial neural networks (ANNs) to model the performance of a subsurface drainage system in Nova Scotia, Canada. The ANN model was built and trained by using measured data on midspan water-table depths and drain outflows from an alfalfa field. The results obtained by the ANN model were compared with the measured data, and with the simulated results from a conventional mathematical model, DRAINMOD. The results show that the ANN model can simulate midspan water-table fluctuations and drain outflows quite well. The ANN model runs significantly faster and requires significantly fewer inputs than DRAINMOD. The ANN simulations depend heavily on the quality of the input data for both average and extreme conditions. This study indicates that an ANN model may be used effectively for the design and evaluation of subsurface drainage systems. The benefits of ANNs are speed, accuracy, ease-of-use and flexibility.
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