Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 167, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2023.105772
Keywords
Event identification; Machine learning; Online platform; Real-time flood forecasting; Urban drainage systems
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This study proposes a novel event-based decision support algorithm for real-time flood forecasting, which achieves higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2-3 hrs), compared to traditional models.
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.
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