Artificial neural networks for subsurface drainage and subirrigation systems in Ontario, Canada

Artificial neural network (ANN) models were developed to simulate fluctuations in midspan water table depths (WTD) given rainfall, potential evapotranspiration, and irrigation inputs on a Brookston clay loam in Woodslee, Ontario, having a dual-purpose subsurface drainage/subirrigation setup. Water table depths and meteorologic data collected at this site from 1992 to 1994 and from 1996 to 1997 were used to train the ANNs. The ANNs were then used for real-time control and time series simulations. The lowest root mean squared errors (RMSE) for the various ANNs were 60.6 mm for real-time control simulation, and 88.4 mm for time-series simulation of water table depths. It was possible to simulate WTD for the different modes of water table management in one network by incorporating an indicator for switching from one to the other. The ANN simulations were quite good even though the training data sets had irregular measurement intervals. With fewer input parameters and small network structures, ANNs still provided accurate results and required little time for training and execution. ANNs are therefore easier and faster to develop and run than conventional models and can contribute to the proper management of subsurface drainage and subirrigation systems.