In this paper we report on the development of an artificial neural network (ANN) model for the design and evaluation of subirrigation systems. The model was formulated and trained by using simulation data from DRAINMOD, a well-known water-table management model. The DRAINMOD model was used to simulate subirrigation in a clay loam soil with 26 years of weather data. One of the main model outputs, the midspan water-table depth, was used to train the ANN model. In addition, the ANN model required data on rainfall, evapotranspiration, weir levels for water-table control, and midspan water-table depths for a learning situation. The results show that the ANN model was able to simulate as well as DRAINMOD. The root mean square (RMS) errors between the ANN and DRAINMOD simulated water-table depths were less than 0.1. Compared to DRAINMOD, the ANN model required very little data to run, and it also executed a lot faster. Therefore, there is a possibility of using such ANN models in real-time control of subirrigation systems, where important decisions need to be made on very short notice. In addition, given the fact that the ANN technology emulated the complex conventional model DRAINMOD, it should be possible to apply this technology to other problems in soil hydrology and contaminant movement.
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