Forecasting Soil Moisture in Caragana Shrubland Using Wavelet Analysis and NARX Neural Network

Abstract

It is important for sustainable use of soil water resource and high-quality development to forecast the soil moisture in forestland of water-limited regions. There are some soil water models. However, there is not the best model to forecast the change of soil moisture in the caragana shrubland. In this paper, the plant water relationship has been investigated at the same time for a long term in the caragana shrubland of semiarid region of the Loess Plateau of China. The data of soil moisture was divided and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted. The results showed the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in 100 cm layer using model II is 0.8%, then soil water content in the 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9% and 0.4%. The results showed that using model II to predict soil water is well Predicting soil water using model II will be important for sustainable use of soil water resource and high-quality development.