In order to improve the speed and accuracy of water level prediction,a prediction model based on improved echo state network (ESN) was proposed. Xavier method was intro-duced to optimize the weights to adapt to the water level pre-diction task. At the same time,the concept drift detection method (EDDM) was introduced to adapt to the actual water level environment,monitor the change of water level data dis-tribution,and trigger the corresponding model update or adap-tation strategy when the concept drift was detected,so as to improve the prediction effect of the real water level. The ex-perimental results of data from nine water level stations show that compared to traditional sequence prediction models (SVM,RNN,GRU,LSTM,ESN,and XESN (Xavier-ESN)),the proposed model shows higher prediction accuracy for each water level station in terms of overall prediction per-formance and short-term,medium-term,and long-term pre-dictions,further improving the prediction accuracy of inland waterway water levels.
维普
万方数据
