Research on flood runoff forecasting based on ensemble learning and deep learning
Deep learning models have demonstrated exceptional capabilities in managing the intricate interactions among hydro-logical factors,leading to their adoption in hydrological forecasting.Nonetheless,there remains a gap in researches on the integra-tion of ensemble learning with deep learning models.This study introduced a novel combined model,termed AdaBoost-Informer model,which integrates the AdaBoost algorithm with the Informer deep learning model to enhance flood runoff forecasting accura-cy.The model utilizes historical precipitation and runoff data as input,with the Informer model,known for its proficiency in captu-ring long-term dependencies,serving as the weak learner within the ensemble framework.Hyperparameters are optimized using grid search,and AdaBoost is employed to weight and aggregate the weak learners into a robust predictor.Evaluation in the Jiao-jiang River Basin in Zhejiang Province revealed that the AdaBoost-Informer model outperforms other models such as Random Forest,AdaBoost,Transformer,and Informer,achieving an RMSE of 62.08 m3/s,an MAE of 23.83 m3/s,an NSE of 0.980,and a forecasting success rate of 100%.This model can significantly enhance the precision of flood forecasts and offer a valuable basis for decision-making in flood prevention and emergency management.
flood runoff forecastingensemble learningdeep learningcombining modelInformer algorithmJiaojiang River Basin
万方数据
维普
