Piadeh, Farzad, Bakhtiari, Vahid, Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408 and Piadeh, Farshad (2026) AI-assisted framework using physically informed rainfall–drainage features for real-time urban flood risk forecasting. Journal of Hydrology, 677. ISSN 0022-1694

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Abstract

Urban flash flooding is becoming more severe as urban population density increases, and severe and frequent floods occur. As a result, early warning systems are required that translate raw sensor data into clear and actionable hazard situations, rather than simply providing ongoing forecasts. For this purpose, this study develops a hydrological- and hydraulics-informed framework for real-time multi-class AI-based flood warning across evaporation (low risk), drained (medium risk), and flooding (high risk) states. The methodology firstly derives physically guided rainfall features using a rule-based back-propagation neural network to estimate return-period signals alongside seasonality and antecedent-rainfall cues, while hydraulics-informed “water-level memory” (current class and class duration) captures system dynamics. These inputs feed seven weak-learner families whose outputs are fused by a time-series mixture-of-experts. The model performance is evaluated for lead times of up to 5 h using both multi-step metrics and event-based analyses. The framework is applied to the Ruislip urban drainage system, UK, using long-term IoT rainfall and water-level records (2011–2024). The results show discrimination improved compared with voting and averaging ensembles. It also increases hit rates for flood and non-flood decisions at 1–3 h. At 4–5 h, it reduces false alarms and late detections. Event-based results show more on-time hits and shorter timing lags. Feature analysis shows that rainfall intensity and duration are the main drivers. Seasonal effects and antecedent occurrence also provide added value. Residual errors concentrate at longer horizons e.g. 5 h later where transitions between adjacent states are intrinsically difficult.

Item Type:	Article
Identifier:	10.1016/j.jhydrol.2026.135819
Keywords:	Drainage systems; Dynamic mixture of expert; Ensemble modelling; Multi-class prediction; Real-time modelling; Urban flood forecasting
Subjects:	Natural sciences > Environmental sciences > Floods Computing > Intelligent systems
Date Deposited:	29 Sep 2026
Dates:	Date Publication status 1 June 2026 Accepted 3 June 2026 Published Online
School, department or research centre:	School of Computing and Engineering
Keywords:	Drainage systems; Dynamic mixture of expert; Ensemble modelling; Multi-class prediction; Real-time modelling; Urban flood forecasting
URI:	https://repository.uwl.ac.uk/id/eprint/15451
Sustainable Development Goals:	Goal 6: Clean Water and Sanitation	Sustainable Development Goals:	Goal 9: Industry, Innovation, and Infrastructure	Sustainable Development Goals:	Goal 11: Sustainable Cities and Communities	Sustainable Development Goals:	Goal 13: Climate Action

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