Development and validation of a neural network model for soil water content prediction with comparison to regression techniques
1999
Altendorf, C.T. | Elliott, R.L. | Stevens, E.W. | Stone, M.L.
A set of neural networks, each of which predicts soil water content at a given depth as a function of soil temperature, has been developed. Networks were developed to predict at depths of 0.15 m, 0.3 m, 0.6 m, and 1.2 m. Input data consists of a coefficient describing soil type and soil temperatures measured at two depths above and two depths below the desired depth. The networks were trained and tested using data from two sites in Oklahoma, and data from a third site were used for additional testing. The networks were generally able to predict the variations in water content over time. Accuracy, as assessed by root mean square error in predicted value, ranged from 0.0142 to 0.0221 m(3) m(-3). Network performance generally improved as deeper depths were considered. For comparison purposes, multiple linear regression models were also estimated. They did not perform well, particularly in following the trends in the data over time.
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