Journal
AGRONOMY-BASEL
Volume 11, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/agronomy11081521
Keywords
soil water dynamic modeling; neural network ensemble; multilayer perceptron; random initialized parameters; soil-plant-atmosphere system
Categories
Funding
- Fundamental Research Funds for the Central Universities [2019B18214]
- China Postdoctoral Science Foundation [2019M651684]
- National Natural Science Foundation of China [51909064]
- Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University [2019490411]
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In this study, a neural network ensemble method was used to predict soil moisture, achieving high accuracy by eliminating the effects of initial parameters on model accuracy.
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.
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