Applications of artificial neural networks to land drainage engineering
1996
Yang, C.C. | Prasher, S.O. | Lacroix, R.
An artificial neural network (ANN) model was developed and trained by using the simulated midspan water table depths from DRAINMOD, a conventional water table management model. Compared to DRAINMOD, the model is very simple to run, and requires only a small amount of data, such as precipitation, evapotranspiration, and initial midspan water table depth. The results indicate that the ANN model can make predictions similar to DRAINMOD, with the least root mean square error of 0.1193, and doing this significantly faster and with fewer input data. The results also indicated that the successful prediction of midspan water table depths depends upon the inclusion of data indicating average as well as extreme conditions, in order to train the ANNs. Given such data, ANNs perform well under general conditions. Generally, the ANN structure with six processing elements and one hidden layer was sufficient for this study. It was found that the networks should be trained with at least 145,000 cycles, but more than 200,000 cycles are unnecessary. A feedback procedure was implemented which fed the previous water table depth output back into the current input. In addition, a lag procedure was suggested which improved the performance of ANNs under irregular situations, such as sudden and large rainstorms. A three-day lag of all input parameters was the best choice when the weather conditions were irregular. The benefits of ANNs are speed, accuracy, ease-of-use and flexibility, thus making ANN models suitable for water table management systems that require a real-time control.
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