A Simulation Study Using Machine Learning and Formula Methods to Assess the Soybean Groundwater Contribution in a Drought-Prone Region

Groundwater contributes to the delivery of phreatic water to crop aeration zones via evapotranspiration, which is important for crop growth in drought-prone regions. Most studies on groundwater contribution have not considered the influence of crop growth stage or daily evapotranspiration. In this study, a neural network based on a genetic algorithm and the Levenberg-Marquardt backpropagation algorithm, as well as formula methods based on an accelerated genetic algorithm, were built to assess soybean groundwater contribution; in addition, a performance comparison was conducted. The results indicated that machine learning had the best performance for fitting errors, with values for relative mean error (RME), root mean square percentage error (RMSPE), and correlation coefficient of 1.088, 2.165, and 0.762, respectively; in addition, for validation errors, it had values for RME, RMSPE, and correlation coefficient of 1.069, 2.136, and 0.735, respectively. The machine learning method is recommended for readers seeking to calculate groundwater contribution.