发表机构
INRAE; ANDRA（法国国家农业、食品与环境研究院; 法国国家放射性废物管理局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨基于Transformer的扩散模型在水文时间序列模拟和重建中的应用，将其用于法国东北部多地点水量和水质联合建模，经校准验证后与其他方法比较，结果支持该方法有效性，凸显其捕捉复杂模式能力。
AI 中文摘要
水文气象时间序列建模面临关键挑战，传统统计方法难以应对。深度学习的进展为复杂时间序列的表示和生成提供了方向。本研究探讨基于Transformer的扩散模型在水文时间序列模拟和重建中的应用。该框架应用于法国东北部三个相邻源头集水区六个地点的水量和水质联合建模。利用15年以上的观测数据校准和验证模型，并与其他方法比较。结果支持基于Transformer方法的有效性，凸显其捕捉和模拟水文数据复杂模式的能力。
英文摘要
The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment. Due to the high variability of the underlying processes and the sparsity of available measurements, traditional statistical approaches often struggle to accurately represent their dynamics. In this context, recent advances in deep learning offer a promising direction for improving the representation and generation of complex temporal processes sampled at several observation sites. This study investigates the application of transformer-based diffusion models to the simulation and reconstruction of hydrological time series. The proposed framework is applied to the joint modeling of water quantity and quality at six sites spread across three adjacent headwater catchments located in North-East France on a limestone plateau covered by forests and field crops. The model is calibrated and validated using available observational data, which has been quality controlled and corrected for sensor drift and malfunction through collaborative efforts by LNE metrology expertise and Andra monthly quality control over more than 15 years. Its performance is compared with several established baseline approaches commonly used for time series modeling. Quantitative evaluation metrics are employed to assess the ability of the proposed method to reproduce key temporal characteristics of the observed signals in two settings: the imputation of incomplete time series and the forecasting of upcoming hydrological conditions. Results support the effectiveness of the transformer-based approach and highlight its capacity to capture and simulate the complex patterns present in hydrological data. In particular, the results indicate that diffusion models can efficiently sample realistic time series distributions under observation settings with variable missing data for both forecasting and imputation.
