A Machine Learning Approach for Forecasting and Visualizing Flood Inundation Information

Syed Kabir, Sandhya Patidar, Gareth Pender

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
211 Downloads (Pure)


This paper presents a new data-driven modelling framework for forecasting probabilistic flood inundation maps for real-time applications. The proposed end-to-end (rainfall-inundation) method combines a suite of machine learning (ML) algorithms to forecast discharge and deliver probabilistic flood inundation maps with a 3 h lead time. To classify wet/dry cells, the method applies rainfall-discharge models based on random forest technique on top of classifiers based on multi-layer perceptron. The hybrid modelling framework was tested using two subsets of data created from an observed fluvial flood event in a small flood-prone town in the UK. The results showed that the model can effectively emulate the outcomes of a hydrodynamic model (Flood Modeller (FM)) with considerably high accuracy measured in terms of flood arrival time error and classification accuracy. The mean arrival time difference between the proposed model and the hydrodynamic model was 1 h 53 min. The classification accuracy was measured against a synthetic aperture radar image, producing accuracies of 88.22% and 86.58% for the proposed data-driven model and FM, respectively. The key features of the proposed modelling framework are that it is simple to implement, detects flooded cells effectively and substantially reduces computational time.

Original languageEnglish
Pages (from-to)27-41
Number of pages15
JournalProceedings of the Institution of Civil Engineers: Water Management
Issue number1
Early online date26 May 2020
Publication statusPublished - Feb 2021


  • computational mechanics
  • floods &floodworks
  • hydrology &water resource

ASJC Scopus subject areas

  • Water Science and Technology


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