基于RWKV-TS的混凝土拱坝位移预测模型研究OA

Accurate prediction of dam deformation is critical to ensuring the structural safety of dams.Dam deformation prediction is essentially a time series forecasting problem involving multiple influencing factors and nonlinear effects.Compared to traditional statistical models,deep learning exhibits a significant advantage in capturing nonlinear features,making it more suitable for dam deformation prediction.This study employs a time series forecasting model based on the RWKV(receptance weighted key value)model,termed the RWKV-TS model.This model integrates the strengths of both Recurrent Neural Networks and Transformers,effectively avoiding the gradient explosion issues associated with RNNs while mitigating the limitations of Transformer in memory consumption and secondary development.Through a case study on the displacement prediction of an arch dam,the RWKV-TS model demonstrates superior performance over traditional models in terms of mean absolute error(EMA),mean absolute percentage error(EMAP),and root mean square error(ERMS),thereby significantly enhancing the accuracy of dam deformation prediction with high efficiency.It offers substantial value for engineering practice and decision-making in management.