水力发电学报2025，Vol.44Issue(4)：97-107,11.DOI:10.11660/slfdxb.20250410
Kolmogorov-Arnold网络在长江中下游水位预报中的应用
Application of Kolmogorov-Arnold networks to water level forecasting in middle and lower Yangtze River
摘要
Abstract
A data-driven water level forecasting method is constructed using Kolmogorov-Arnold Networks(KAN),which decomposes the complex relationships among hydrological variables into a linear combination of univariate functions,enabling accurate capture of the trends in hydrological data.The method has been applied to water level forecasting based on discharge and water level data from the Lianhuatang and Shashi stations in the middle and lower Yangtze River.Results show the KAN model has a seven-day mean absolute error of 0.187 m at Lianhuatang and 0.109 m at Shashi.In the case of Lianhuatang,it improves forecasting accuracy by 20.1%,45.0%,16.5%,and 13.0%compared to traditional Multi-Layer Perceptron,Long Short-Term Memory network,Gated Recurrent Unit network,and Transformer models,respectively.To deepen our understanding of this model further,sensitivity analysis and simplification tests are conducted.Results indicate its short-term upstream discharge forecasting significantly affects the predicted downstream water levels.Equipped with a minimal number of parameters,it achieves effectively the relationship between upstream discharge and downstream water level changes,demonstrating remarkable interpretability.关键词
水位预报/Kolmogorov-Arnold网络/机器学习/长江中游/可解释性Key words
water level forecasting/Kolmogorov-Arnold networks/machine learning/middle reaches of the Yangtze River/interpretability分类
水利科学引用本文复制引用
陈思宇,李肖男,花续,鲁军,荆平飞,宋志豪..Kolmogorov-Arnold网络在长江中下游水位预报中的应用[J].水力发电学报,2025,44(4):97-107,11.基金项目
国家重点研发计划项目(2022YFC3002705) （2022YFC3002705）
