Research on VMD-HKELM Monthly Evaporation Prediction Based on Gold Rush Algorithm Optimization
The prediction of water surface evaporation is of great significance for reservoir storage prediction,regional water balance analysis,and water resources accounting.There are numerous factors that affect the prediction of water surface evaporation,which ulti-mately manifest in evaporation monitoring data that vary over time.To address this,two schemes were proposed based on the Gold Rush Optimization(GRO)algorithm to optimize the VMD Hybrid Kernel Extreme Learning Machine(HKELM).Scheme Ⅰ decomposed the monthly evaporation data using time series analysis and then dividing it into training and testing sets.Scheme Ⅱ divided the monthly evaporation data into training and testing sets before applying time series decomposition.A novel meta-heuristic algorithm was used to optimize the hyper parameters of both the decomposition technique(VMD)and the predictor(HKELM),establishing multiple models.These models were tested using monthly evaporation data from Longtan village and Western Street hydrologic stations in Yun-nan Province for both Scheme Ⅰ and Scheme Ⅱ.The results indicate that the models in Scheme Ⅰ outperform those in Scheme Ⅱ.Overall,the fitting and prediction accuracy of each model increase with the number of decomposition components.However,it should be noted that Scheme Ⅰ utilizes information from the testing set,leading to inflated prediction accuracy.In contrast,the models in Scheme Ⅱ demonstrate good prediction accuracy and robustness,making them feasible for predicting monthly evaporation time series.These models reflect objective and realistic prediction results,demonstrating their practical value and significance.
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