Evaluating the Neural Network Ensemble Method in Predicting Soil Moisture in Agricultural Fields

Soil is an important element in the agricultural domain because it serves as the media that bridges the water consumption and supply processes. In this study, a neural network ensemble (NNE) method was employed to predict the soil moisture to eliminate the effects of random initial parameters of neural network (NN) on model accuracy. The constructed NNE model predicts daily root zone soil moisture continuously for the whole crop growing season and the water consumption and supply processes were separately modeled. The soil profile was divided into multiple layers and modeled separately. Weather data (including air temperature, humidity, wind speed, net radiation, and precipitation), rooting depth, and the hesternal soil moisture of each layer were used as the input. A calibrated root zone water quality model for maize (Zea mays L.) was used to generate training and evaluation data. The result showed that with 100 randomly initialized NN models, the NNE model achieved an average R-2 of 0.96 and nRMSE of 5.93%, suggesting that the NNE model learned the soil moisture dynamics well and sufficiently improved the robustness of soil moisture prediction with high accuracy.