基于改进ESN的内河航道水位预测模型  

A water level prediction model for inland waterways based on improved ESN

作　　者：张文儒 李超[1] 刘宗鹰 潘明阳[1] 李非凡 ZHANG Wenru;LI Chao;LIU Zongying;PAN Mingyang;LI Feifan(Navigation College,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学航海学院,辽宁大连116026

出　　处：《大连海事大学学报》2024年第3期104-112,共9页Journal of Dalian Maritime University

基　　金：国家自然科学基金面上项目(52371363);中央高校基本科研业务费专项资金资助项目(2023JXA(07))。

摘　　要：为提高内河航道水位预测速度和准确性,提出基于改进回声状态网络(ESN)的预测模型,引入Xavier方法进行权重优化以适应水位预测任务,同时,引入概念漂移检测方法(EDDM)适应实际水位环境,监测水位数据的分布变化,在检测到概念漂移时触发相应的模型更新或适应策略,提高对现实水位的预测效果。对九个水位站的数据实验结果显示,本文模型相较传统序列预测模型(SVM、RNN、GRU、LSTM、ESN及XESN(Xavier⁃ESN)),在整体预测性能以及短期、中期、长期预测中,对每个水位站的预测均表现出更高的预测精度,进一步提升了内河航道水位的预测精度。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 introduced to optimize the weights to adapt to the water level prediction 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 distribution,and trigger the corresponding model update or adaptation strategy when the concept drift was detected,so as to improve the prediction effect of the real water level.The experimental 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 performance and short⁃term,medium⁃term,and long⁃term predictions,further improving the prediction accuracy of inland waterway water levels.

关 键 词：内河航道 水位预测 回声状态网络(ESN) 概念漂移 早期漂移检测方法(EDDM) 

分 类 号：U644[交通运输工程—船舶及航道工程]