Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China

The accurate estimation of crop evapotranspiration (ETc) is essential for precision irrigation, optimal allocation of regional water resources, and efficiency improvement of agricultural water resources. This study developed Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) models for maize ETc estimation in northwest China. The meteorological data and crop data from 2011 to 2012 were used to train the RF, SVM, ANN and ELM. The models' simulation accuracy was verified by using the data of 2013 under six different input combinations. The input combinations included daily data for crop coefficient (K-c), global solar radiation (R-s), wind speed (u(2)), maximum and minimum air temperatures (T-max and T-min), and maximum and minimum relative humidity (RHmax and RHmin). The results showed that the SVM model achieved the highest simulation accuracy at the seedling emergence to jointing stage and at the grouting to harvest stage of summer maize, with the coefficient of determination (R2) ranging 0.701-0.895 and 0.637-0.841, mean absolute error (MAE) ranging 0.310-0.654 and 0.468-0.743 mm/d, and mean square error (MSE) ranging 0.227-0.722 and 0.513-1.227 mm/d, respectively. The ELM model achieved the highest simu-lation accuracy at the booting to silking stage and during the whole growth period, the coefficient of determi-nation (R-2) ranging 0.601-0.828 and 0.891-0.954, mean absolute error (MAE) ranging 0.418-1.194 and 0.285-0.530 mm/d, and mean square error (MSE) ranging 0.887-2.515 and 0.182-0.587 mm/d, respectively. Considering the accessibility and simulation accuracy of input parameters, the SVMI-2, ELMII-5, SVMIII-4, and ELMIV-2 models were recommended for simulating ETc at the seedling emergence to jointing stage, at the booting to silking stage, at the grouting to harvest stage, and during the whole growth period, with the coefficient of determination (R-2) of 0.796, 0.879, 0.800 and 0.896, mean absolute error (MAE) of 0.416, 0.418, 0.553 and 0.328 mm/d, and mean square error (MSE) of 0.327, 0.887, 0.655 and 0.190 mm/d, respectively. In conclusion, machine learning models can accurately simulate the daily evapotranspiration of maize in northwest China.

Automated summary

This study developed machine learning models including Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Learning Machine for estimating maize evapotranspiration in northwest China. The results showed that the Support Vector Machine model achieved the highest simulation accuracy at certain growth stages, while the Extreme Learning Machine model achieved the highest simulation accuracy at different stages.