基于多层回声核状态网络的内河水位预测

doi:10.16411/j.cnki.issn1006-7736.2025.03.007

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大连海事大学学报 ›› 2025, Vol. 51 ›› Issue (3) : 64-73. DOI: 10.16411/j.cnki.issn1006-7736.2025.03.007

基于多层回声核状态网络的内河水位预测

王淦，李邵喜*，刘宗鹰，温春森

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Multi-Layer echo kernel state network based inland waterway water levels forecasting

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摘要

针对传统水位预测模型难以有效捕捉复杂的非线性动态特征，影响预测精度的问题，提出基于多层回声核状态网络（ML-EKSN）的水位预测模型，利用高斯核函数实现高维非线性映射，并通过多层结构进行层次化特征提取，显著提升了模型的动态建模能力。基于松花江流域7个站点的实测数据，利用ML-EKSN根据30步历史数据预测未来7步水位特征。结果表明，ML-EKSN性能优于LSTM、GRU、RVFL、ESN、EKSN及Transformer等主流模型,可为内河航道水位预测提供高效稳定的解决方案，具备良好的实际应用价值。

Abstract

Aiming at the problem that traditional water level forecasting models are difficult to effectively capture complex nonlinear dynamics features and affect prediction accuracy,a water level forecasting model based on multi-layer echo kernel state network (ML-EKSN) was proposed. Gaussian kernel functions was used to achieve high-dimensional nonlinear mapping, and hierarchical feature extraction was carried out through multi-layer structure, significantly improving the dynamic modeling ability of the model. Based on the measured data from 7 stations in the Songhua River Basin, ML-EKSN was used to predict the future 7 step water level characteristics based on 30 step historical data.The r esults show that the performance of ML-EKSN is superior to mainstream models such as LSTM, GRU, RVFL, ESN, EKSN,and Transformer, and can provide an efficient and stable solution for predicting water levels in inland waterways and has good practical application value.

导出引用

王淦, 李邵喜, 刘宗鹰, 温春森. 基于多层回声核状态网络的内河水位预测[J]. 大连海事大学学报. 2025, 51(3): 64-73 https://doi.org/10.16411/j.cnki.issn1006-7736.2025.03.007

Multi-Layer echo kernel state network based inland waterway water levels forecasting[J]. Journal of Dalian Maritime University. 2025, 51(3): 64-73 https://doi.org/10.16411/j.cnki.issn1006-7736.2025.03.007

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