多特征时空融合网络的水位预测技术

水位高低与航道通航能力息息相关，对大型船舶（队）通过某些典型航段时尤为重要。水位受到多种因素的影响，开发准确、可靠的水位预测模型是一项具有挑战性的问题。提出一种基于Transformer的多特征时空融合网络的水位预测模型，该模型能够捕捉水位数据的复杂时空模式和相互作用，研究水位与不同影响因素的关联关系，根据融合后的特征生成未来水位预测结果。研究成果有助于保障船舶航行安全，充分发挥航道通航能力，为航运管理和规划提供参考。

Water level prediction technique based on multi-feature spatio-temporal fusion network

Water level is closely related to the navigability of a waterway，and is particularly important for large ships(convoys)passing through certain typical sections.Water level is affected by many factors，and developing an accurate and reliable water level prediction model is a challenging problem.In this paper，a water level prediction model based on Transformer''s multi-feature spatio-temporal fusion network is proposed，which is able to capture the complex spatio-temporal patterns and interactions of the water level data，study the correlation relationship between the water level and the different influencing factors，and generate the future water level prediction results based on the fused features.The research results are helpful to ensure the navigation safety of ships，give full play to the navigation capacity of waterways，and provide references for shipping management and planning.