Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model

This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET0 prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. Additionally, a novel evapotranspiration prediction model, Kmeans-QPSO-MKELM, is proposed, incorporating K-means clustering to estimate the daily evapotranspiration in Yancheng, Jiangsu Province, China. In the input selection process, based on the variance and correlation coefficients of various meteorological factors, eight input models are proposed, attempting to incorporate the sine and cosine values of the date. The new model is then subjected to ablation and comparison experiments. Ablation experiment results show that introducing K-means clustering improves the model’s running speed, while the improved QPSO algorithm and the introduction of multiple kernel functions enhance the model’s accuracy. The improvement brought by introducing multiple kernel functions was especially significant when wind speed was included. Comparison experiment results indicate that the new model’s prediction accuracy is significantly higher than all other comparison models, especially after including date sine and cosine values in the input. The new model’s running speed is only slower than the RF model. Therefore, the Kmeans-QPSO-MKELM model, using date sine and cosine values as inputs, provides a fast and accurate new approach for predicting evapotranspiration.