AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer

• URL: http://arxiv.org/abs/2509.09458v1
• Date: Thu, 11 Sep 2025 13:42:34 GMT
• Title: AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
• Authors: Golnoosh Abdollahinejad, Saleh Baghersalimi, Denisa-Andreea Constantinescu, Sergey Shevchik, David Atienza,
• Abstract summary: This work addresses the challenge of forecasting urban water dynamics by developing a multi-input, multi-output deep learning model.<n>The model, AquaCast, captures both inter-variable and temporal dependencies across all inputs.<n>We evaluate our approach on the LausanneCity dataset, which includes measurements from four urban drainage sensors.
• Score: 3.3333213240832134
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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