Comparison of autoregression and neural network models for soil water content forecasting

Soil water dynamics is a complex time series system with obviously random fluctuation, closely related to regional climate and ecological environment. Establishing the model of soil water dynamics can not only modulate real time farm soil water, but also is available to farm irrigation works. In this paper, the autoregression and neural network were applied to establish the model of purple soil water forecast in hilly region. The result showed that: in the case of less data, the autoregression model can preferably fit the soil water time series and its forecasting was available. In the case of enough data, the neural network model could do it better.