Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Piadeh, Farzad (University of West London)
Behzadian, Kourosh (University College London (UCL); University of West London)
Chen, Albert S. (University of Exeter)
Campos, Luiza C. (University College London (UCL))
Rizzuto, Joseph P. (University of West London)
Kapelan, Z. (TU Delft Sanitary Engineering)

2023

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for real-time flood forecasting using event-based data identification, event-based dataset generation, and a real-time decision tree flowchart using machine learning models. The results of applying the framework to a real-world case study demonstrate higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2–3 hrs), compared to traditional models. The proposed framework reduces root mean square error by 50%, increases accuracy of flood forecasting by 50%, and improves normalised Nash–Sutcliffe error by 20%. The proposed event-based dataset framework can significantly enhance the accuracy of flood forecasting, reducing the occurrences of both false alarms and flood missing and improving emergency response systems.

Event identification
Machine learning
Online platform
Real-time flood forecasting
Urban drainage systems

http://resolver.tudelft.nl/uuid:4d7a8ce8-a028-4ad7-bec9-bca278ef8b09

https://doi.org/10.1016/j.envsoft.2023.105772

1364-8152

Environmental Modelling & Software, 167

Institutional Repository

journal article

© 2023 Farzad Piadeh, Kourosh Behzadian, Albert S. Chen, Luiza C. Campos, Joseph P. Rizzuto, Z. Kapelan