用降水信息提升城市水文预测精度，模型更准且可扩展。
AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
- 融合水位、流量与降水数据，通过嵌入层捕捉多源输入关联
- 在真实和合成数据上均超越现有方法，尤其在100节点场景下表现稳定
- 适合城市防洪预警、智慧水务系统研发人员参考
本文针对城市水文动态预测难题，提出多输入多输出深度学习模型AquaCast，同时整合内生变量（如水位、流量）与外生因素（如降水历史和预报报告）。该模型通过嵌入层融合外生输入，无需对其建模，从而更高效地关注其短期影响，并同时捕捉各变量间及时间维度的依赖关系。在包含4个排水传感器的洛桑城市数据集上，仅使用内生变量即达当前最优性能；加入外生变量后效果进一步提升。为验证泛化能力与可扩展性，模型在三个大规模合成数据集上测试：基于MeteoSwiss记录、Lorenz吸引子模型和随机场模型生成，每组含100个节点，代表不同时间复杂度。结果表明，AquaCast在真实与合成数据上均持续优于基线模型，保持高鲁棒性和准确性。
原文摘要 · Abstract (English)
This work addresses the challenge of forecasting urban water dynamics by developing a multi-input, multi-output deep learning model that incorporates both endogenous variables (e.g., water height or discharge) and exogenous factors (e.g., precipitation history and forecast reports). Unlike conventional forecasting, the proposed model, AquaCast, captures both inter-variable and temporal dependencies across all inputs, while focusing forecast solely on endogenous variables. Exogenous inputs are fused via an embedding layer, eliminating the need to forecast them and enabling the model to attend to their short-term influences more effectively. We evaluate our approach on the LausanneCity dataset, which includes measurements from four urban drainage sensors, and demonstrate state-of-the-art performance when using only endogenous variables. Performance also improves with the inclusion of exogenous variables and forecast reports. To assess generalization and scalability, we additionally test the model on three large-scale synthesized datasets, generated from MeteoSwiss records, the Lorenz Attractors model, and the Random Fields model, each representing a different level of temporal complexity across 100 nodes. The results confirm that our model consistently outperforms existing baselines and maintains a robust and accurate forecast across both real and synthetic datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。