Using Ensemble Data Mining Modelling For Nonbinary Overflow Detection In Urban Flooding

This study presents a new real-time nonbinary overflow detection in urban flooding through extraction of rainfall key features by developing weak learner base models and proposing time-series multi-classification ensemble model. This framework is demonstrated by its application on real case study of urban drainage systems (UDS) located in London, UK. The application of data-driven modelling, especially using data mining techniques, in flood warning systems has been gaining attention due to its sustainable solution for alleviating the disruptive socio-economic effects of flood occurrence. The proposed framework is expected to provide a reliable and accurate prediction of flood risk and overflow detection.