Short-Term Forecast of Yangtze River Discharge Based on Improved ROA-LSTM Time Series
To enhance the accuracy of short-term predictions regarding the temporal variations in Yangtze River flow,and to address chal-lenges related to parameter selection as well as the propensity for traditional LSTM models to converge on local optimum solutions in time series forecasting,a hybrid model known as ROA-LSTM is developed.This model integrates an improved ROA optimization algorithm with an attention mechanism alongside the WOA and SFO algorithm,effectively combining the ROA optimization technique with the LSTM framework.The predictive performance of the proposed model is evaluated against actual Yangtze River flow data obtained from an acoustic tomography system.In terms of short-term forecasts spanning three days or less,the proposed model demonstrates a 2-3 fold increase in prediction accuracy compared to conventional RNN models.Furthermore,it exhibits superior capabilities in both trends forecasting in flow changes and peak predictions.
deep learningshort-flow forecastingenhanced ROA-LSTMattention mechanismparameter optimization
万方数据
维普
