Integrating Precipitation Nowcasting in a Deep Learning-Based Flash Flood Prediction Framework and Assessing the Impact of Rainfall Forecasts Uncertainties

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dc.contributor.advisorErechtchoukova, Marina G.
dc.contributor.authorMhedhbi, Rim
dc.date.accessioned2022-12-14T16:19:33Z
dc.date.available2022-12-14T16:19:33Z
dc.date.copyright2022-05-13
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:19:33Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractFlash floods are among the most immediate and destructive natural hazards. To issue warnings on time, various attempts were made to extend the forecast horizon of flash floods prediction models. Particularly, introducing rainfall forecast into process-based hydrological models was found effective. However, integrating precipitation predictions into flash flood data-driven models has not been addressed yet. In this endeavor, we propose a modeling framework that integrates rainfall nowcasts and assesses the impact of rainfall predictions uncertainties on a Deep Learning-based flash flood prediction model. Compared to the Persistence and ARIMA models, the LSTM model provided better rainfall nowcasting performance. Further, we proposed an Encoder-Decoder LSTM-based model architecture for short-term flash flood prediction that supports rainfall forecasts. Computational experiments showed that future rainfall values improved flash floods’ predictability for extended lead times. We also found that rainfall underestimation had a significant adverse effect on the model’s performance compared to rainfall overestimation.
dc.identifier.urihttp://hdl.handle.net/10315/40607
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subject.keywordsFlash flood
dc.subject.keywordsLSTM
dc.subject.keywordsUncertainty quantification
dc.subject.keywordsPrecipitation nowcasting
dc.subject.keywordsMachine learning
dc.subject.keywordsData analysis
dc.subject.keywordsDeep learning
dc.subject.keywordsData-driven modeling
dc.titleIntegrating Precipitation Nowcasting in a Deep Learning-Based Flash Flood Prediction Framework and Assessing the Impact of Rainfall Forecasts Uncertainties
dc.typeElectronic Thesis or Dissertation

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