Compared with other types of floods, timely and accurately predicting flash floods is particularly challenging due to the small spatiotemporal scales in which the hydrologic and hydraulic processes tend to develop, and to the short lead time between the causative event and the inundation scenario. With continuous increased availability of data and computational power, the interest in applying techniques based on machine learning for hydrologic purposes in the context of operational forecasting has also been increasing. The primary goal of the research activities developed in the context of this thesis is to explore the use of emerging machine learning techniques for enhancing flash flood forecasting. The studies presented start with a review on the state-of-the-art of documented forecasting systems suitable for flash floods, followed by an assessment of the potential of using multiple concurrent precipitation estimates for early prediction of high-discharge scenarios in a flashy catchment. Then, the problem of rapidly producing realistic highresolution flood inundation maps is explored through the use of hybrid machine learning models based on Non-linear AutoRegressive with eXogenous inputs (NARX) and SelfOrganizing Maps (SOM) structures as surrogates of a 2D hydraulic model. In this context, the use of k-fold ensemble is proposed and evaluated as an approach for estimating uncertainties related to the surrogating of a physics-based model.
The results indicate that, in a small and flashy catchment, the abstract nature of data processing in machine learning models benefits from the presentation of multiple concurrent precipitation products to perform rainfall-runoff simulations when compared to the business-as-usual single-precipitation approach. Also, it was found that the hybrid NARX-SOM models, previously explored for slowly developing flood scenarios, present acceptable performances for surrogating high-resolution models in rapidly evolving inundation events for the production of both deterministic and probabilistic inundation maps in which uncertainties are adequately estimated. / Thesis / Doctor of Science (PhD) / Flash floods are among the most hazardous and impactful environmental disasters faced by different societies across the globe. The timely adoption of mitigation actions by decision makers and response teams is particularly challenging due to the rapid development of such events after (or even during) the occurrence of an intense rainfall. The short time interval available for response teams imposes a constraint for the direct use of computationally demanding components in real-time forecasting chains. Examples of such are high-resolution 2D hydraulic models based on physics laws, which are capable to produce valuable flood inundation maps dynamically. This research explores the potential of using machine learning models to reproduce the behavior of hydraulic models designed to simulate the evolution of flood inundation maps in a configuration suitable for operational flash flood forecasting application. Contributions of this thesis include (1) a comprehensive literature review on the recent advances and approaches adopted in operational flash flood forecasting systems with the identification and the highlighting of the main research gaps on this topic, (2) the identification of evidences that machine learning models have the potential to identify patterns in multiple quantitative precipitation estimates from different sources for enhancing the performance of rainfall-runoff estimation in urban catchments prone to flash floods, (3) the assessment that hybrid data driven structures based on self-organizing maps (SOM) and nonlinear autoregressive with exogenous inputs (NARX), originally proposed for large scale and slow-developing flood scenarios, can be successfully applied on flashy catchments, and (4) the proposal of using k-folding ensemble as a technique to produce probabilistic flood inundation forecasts in which the uncertainty inherent to the surrogating step is represented.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28205 |
| Date | January 2022 |
| Creators | Della Libera Zanchetta, Andre |
| Contributors | Coulibaly, Paulin, Civil Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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