Piadeh, Farzad ORCID: https://orcid.org/0000-0002-4958-6968, Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408, Chen, Albert, Campos, Luiza, Rizzuto, Joseph and Kapelan, Zoran (2023) Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling. Environmental Modelling & Software, 167 (2023). ISSN 1364-8152

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Abstract

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.

Item Type:	Article
Identifier:	10.1016/j.envsoft.2023.105772
Keywords:	Event identification; Machine learning; Online platform; Real-time flood forecasting; Urban drainage systems
Subjects:	Computing
Depositing User:	Farzad Piadeh
Date Deposited:	18 Sep 2023 12:18
Last Modified:	04 Nov 2024 11:03
URI:	https://repository.uwl.ac.uk/id/eprint/10256

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