VMD-IFDA-NAHL模型在某水文站日径流预测中的应用OA

Conducting research on high-precision runoff prediction models is crucial in providing a reliable data support for water resource management and flood control.For daily runoff prediction,we designed a variational mode decomposition-improved flow direction algorithm network with an augmented hidden layer(VMD-IFDA-NAHL).This model first employs variational mode decomposition(VMD)to preprocess daily runoff data from the hydrological station,removing noise and extracting latent nonlinear features within the runoff data.Subsequently,an improved flow direction algorithm(IFDA)is applied to optimize the auto-neural network model with an augmented hidden layer,thereby improving the model's ability to capture runoff variations and improve prediction accuracy.Using the hydrological station as a case study,the VMD-IFDA-NAHL model and a reference model are compared in terms of daily runoff prediction.The results show that the VMD-IFDA-NAHL model outperforms the reference model in terms of accuracy,stability,and generalization ability in runoff prediction,indicating that it has a wide range of applications in runoff forecasting.Therefore,the daily runoff prediction model developed can provide reliable and scientific data support for water resource management,flood control regulation,and other related fields.