Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China

The accurate prediction of daily reference crop evapotranspiration (ET O ) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ET O forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ET O at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ET O , and the range of importance is 0.399–0.554. RH and Ra are also key factors influencing ET O ; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408–1.964, R 2 of 0.545–0.982, mean absolute error of 0.273–1.086, and Nash–Sutcliffe efficiency coefficient of 0.658–0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ET O estimation in arid and semiarid regions of China, and this model can also serve as a reference for ET O forecasting in similar regions.