论文信息 - AN ARTIFICIAL NEURAL NETWORK MODEL FOR WATER TABLE MANAGEMENT SYSTEMS

AN ARTIFICIAL NEURAL NETWORK MODEL FOR WATER TABLE MANAGEMENT SYSTEMS

This paper presents the development of an artificial neural network (ANN) model to simulate fluctuations in midspan water table depths, influenced by daily rainfall and potential evapotranspiration rates. Unlike conventional mathematical models, ANN models do not require, a priori, explicit knowledge of the relationship between inputs and outputs. This knowledge is obtained through training: field observations of inputs and outputs. Compared with conventional mathematical models, ANN models require fewer input parameters and can be run quickly on a PC. These benefits prove to be significant in the real-time control of subsurface drainage and subirrigation systems. In this study, ANN models are developed to simulate water table depths during subsurface drainage and subirrigation. They were trained and tested using field observations on water table depths at an agricultural field in Woodslee, Ontario, from 1992 to 1994. The results show the applicability of ANNs in drainage modeling. They simulate water tab...

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