基于PSO-SAE-XGB的多模型水文预报OA

To address problems in hydrological forecasting,such as complex multi-source runoff generation mechanisms and significant model structural uncertainties,a novel multi-model coupled forecasting framework was developed.The goal of this study was to improve prediction accuracy and robustness by incorporating multiple machine learning models,advanced feature extraction,and intelligent optimization algorithms.The Niuchang section of the Baoku River in the Huangshui River basin was chosen as the case study.Daily rainfall and runoff data were collected over a 23-year period(2002-2024),with the first 17 years used for model training and the remaining 6 years for validation. The PSO-SAE-XGB forecasting methodology,which combines multiple models,was proposed and implemented.Using their complementary strengths in bias-variance trade-off and nonlinear fitting,five well-known machine learning models-AdaBoost,Bagging,Histogram Gradient Boosting(HistGradient),random forest(RF),and back propagation neural network(BP)-were used as base predictors to produce a variety of runoff forecasts.A stacked autoencoder(SAE)was then used to extract deep features from the concatenated predictions of these five base models.By reconstructing the input,the SAE sought to learn robust,high-level sparse representations that would improve the feature space for the final integration and filter out model-specific noise.The extracted latent features from the SAE were then fed into an extreme gradient boosting(XGB)model for nonlinear ensemble forecasting.To optimize the overall framework,a particle swarm optimization(PSO)algorithm was introduced to jointly calibrate the key hyperparameters of both the SAE(e.g.,number of units in each hidden layer)and the XGB model(e.g.,max depth,learning rate).This joint optimization ensured synergistic performance between the feature extraction and regression stages.The model's performance was evaluated using the Nash-Sutcliffe efficiency coefficient(ENS),root mean square error(ERMS),and mean absolute error(EMA). The results demonstrated the effectiveness of the proposed approach.All of the base models were evaluated and found to produce reasonable forecasts,with HistGradient outperforming the others(validation ENS=0.921).The SAE successfully reconstructed the outputs of all base models with high fidelity(validation ENS>0.859 for all),effectively extracting essential features and proving particularly effective for ensemble-based models like RF and AdaBoost.The integrated PSO-SAE-XGB model significantly outperformed both the individual base models and a counterpart model without SAE(PSO-XGB).During the validation period,the PSO-SAE-XGB model achieved an ENS of 0.976 and an ERMS of 0.840,surpassing the PSO-XGB model's ENS of 0.952 and ERMS of 1.484.Importantly,the PSO-SAE-XGB model exhibited superior generalization capability,as indicated by a much smaller gap between its training and validation ENS(0.003)compared to the PSO-XGB model(0.019).The PSO-SAE-XGB model produced more accurate and stable predictions,particularly during medium to high flow periods when other models displayed greater scatter and deviation,according to a visual analysis of the hydrographs. The accuracy and dependability of runoff forecasting in the examined basin were found to be significantly improved by the PSO-SAE-XGB multi-model coupling framework.The integration of SAE for deep feature extraction was identified as a critical factor in improving model generalization and stability by processing the multi-model predictions into a more informative and denoised feature set.The utilization of PSO for the joint hyperparameter optimization of SAE and XGB ensured an optimal configuration of the entire system.This study presents a robust and advanced technical methodology for hydrological forecasting in complex environments,which is useful for flood control and water resource management.