Artificial neural network model for subsurface-drained farmlands

This paper describes the development of an artificial neural network (ANN) model to simulate fluctuations in midspan water-table depths and drain outflows, as influenced by daily rainfall and potential evapotranspiration rates. Unlike conventional models, ANN models do not require explicit relationships between inputs and outputs. Instead, ANNs map the implicit relationship between inputs and outputs through training by field observations. Compared with conventional models, the ANN model requires fewer input parameters since the inputs that remain constant are not considered by ANNs. Therefore ANNs can be executed quickly on a microcomputer. These benefits can be exploited in the real-time control of water-table management systems. The model was developed using field observations of water-table depths from 1991 to 1993 and drain outflows from 1991 to 1994 made at an agricultural field in Ottawa, Canada. The root mean squared errors and standard deviation of errors of simulated results were found to range from 46.5 to 161.1 mm and 46.6 to 139.2 mm, respectively, thus showing potential applications of ANNs in land drainage engineering.