Title: AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer Show Chinese title

Title: AquaCast：基于降水信息的多输入Transformer城市水文动态预测

Abstract: 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. Show Chinese abstract

Abstract: 这项工作通过开发一个多输入、多输出的深度学习模型来解决预测城市水文动态的挑战，该模型结合了内生变量（例如水位或流量）和外生因素（例如降水历史和预报报告）。 与传统预测不同，所提出的模型AquaCast能够捕捉所有输入之间的变量间和时间依赖性，同时仅将预测集中在内生变量上。 通过嵌入层融合外生输入，消除了预测它们的需要，并使模型能够更有效地关注它们的短期影响。 我们在LausanneCity数据集上评估了我们的方法，该数据集包含来自四个城市排水传感器的测量数据，并且仅使用内生变量时展示了最先进的性能。 包括外生变量和预报报告也能提高性能。 为了评估泛化能力和可扩展性，我们还对三个大规模合成数据集进行了测试，这些数据集是从MeteoSwiss记录、Lorenz吸引子模型和随机场模型生成的，每个数据集代表 across 100 nodes 不同的时间复杂度水平。 结果证实，我们的模型始终优于现有基线，并在真实和合成数据集上保持稳健和准确的预测。