发表机构
University of Padua（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合谐波分析与数据驱动模型的混合框架用于逐小时海平面预报，在六个验潮站评估显示平均误差降低52.9%-54.9%，其中广义可加模型表现最佳，并应用于威尼斯MoSE防洪系统决策。
AI 中文摘要
准确的潮汐预报对于海岸管理、航行、降低洪水风险和基础设施保护至关重要。观测到的海平面可分解为天文和非天文分量，后者主要由气象效应驱动。本研究探讨了一种用于逐小时海平面预报的混合框架，该框架将谐波分析（HA）用于天文分量，并将数据驱动模型用于非天文分量。该方法在六个具有不同潮汐状况的验潮站进行了评估：威尼斯、的里雅斯特、圣马洛、瓦尔德、尼基斯基和长崎。考虑了四类数据驱动模型：（i）线性参数模型，由带外生变量的自回归模型表示；（ii）函数型参数模型，基于带外生变量的函数型自回归模型；（iii）半参数和非线性模型，包括广义可加模型和自回归神经网络；以及（iv）一种半函数非标准k近邻方法，该方法结合了近期非天文轨迹和气象条件的相似性。结果表明，相对于HA，混合模型平均将预报误差降低了52.9%-54.9%。广义可加模型在各地点中表现最具竞争力，而k近邻在圣马洛表现最佳，带外生变量的自回归模型在长崎受到青睐。对于威尼斯，一项经济决策案例研究评估了海平面预报在管理MoSE防洪屏障系统中的操作用途。
英文摘要
Accurate tide forecasts are essential for coastal management, navigation, flood-risk reduction, and infrastructure protection. Observed sea level can be decomposed into astronomical and non-astronomical components, the latter mainly driven by meteorological effects. This study investigates a hybrid framework for hourly sea-level forecasting that combines harmonic analysis (HA) for the astronomical component with data-driven models for the non-astronomical contribution. The approach is evaluated at six tide-gauge stations with different tidal regimes: Venice, Trieste, Saint-Malo, Vardø, Nikiski, and Nagasaki. Four data-driven model classes are considered: (i) linear parametric models, represented by autoregressive models with exogenous variables; (ii) functional parametric models, based on functional autoregressive models with exogenous variables; (iii) semiparametric and nonlinear models, including generalized additive models and autoregressive neural networks; and (iv) a semi-functional non-standard k-nearest-neighbours approach combining similarity in recent non-astronomical trajectories and meteorological conditions. Results reveal that hybrid models reduce forecast errors by 52.9-54.9% on average relative to HA. The generalized additive model is the most competitive across locations, while k-nearest neighbours performs best at Saint-Malo and the autoregressive model with exogenous variables is favoured in Nagasaki. For Venice, an economic decision-making case study assesses the operational use of sea-level forecasts in managing the MoSE flood-barrier system.
