Hydraulic modelling of inland urban flooding: Recent advances

Mignot, Emmanuel; Dewals, Benjamin

doi:10.1016/j.jhydrol.2022.127763

Cited by 57 publications

(17 citation statements)

References 108 publications

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“…[ 15 ]. In addition, the calculation of runoff and confluence, model construction and parameter setting mainly depend on modeler’s understanding of the actual hydrological process, resulting in certain subjectivity and uncertainty in the modeling process of the physical model [ 16 ]. With the development of artificial intelligence and deep learning technology, runoff series prediction by data-driven method has become popular [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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“…Flood inundation of urban areas (possibly induced by a dam-break or a tsunami invading a city) is a research topic that arouses considerable interest nowadays due to the high exposure of residential or industrial settlements close to waterways, dams, or coastal areas [341,342]. Table 4 lists the studies on urban flooding conducted through experimental modelling.…”

Section: Discussion and Advancesmentioning

confidence: 99%

“…More insight into urban flooding could come from considering quasi-realistic urban district models [255], taking into account additional events associated with urban floods [342], such as the penetration of water into buildings through openings [221], the flow exchange between the streets and the sewer system, the transport of cars or urban debris [249], and the diffusion of pollutants. Experimental data from such experiments would better support the validation of urban flood simulation models, which have become increasingly sophisticated in recent years [341]. Among these numerical models, the coarse-grid ones (for example based on the porosity approach [248,252]) can provide accurate results preserving computational efficiency.…”

Section: Discussion and Advancesmentioning

confidence: 99%

Review of Experimental Investigations of Dam-Break Flows over Fixed Bottom

Aureli

Maranzoni

Petaccia

et al. 2023

Water

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Laboratory experiments of dam-break flows are extensively used in investigations of geophysical flows involving flood waves, to provide insight into relevant aspects of the physics of the process and collect experimental data for validating numerical models. A dam-break flow is a typical example of a highly unsteady free surface flow with high reproducibility. Indeed, dam-break experiments can be repeated several times under the same test conditions obtaining large amounts of different types of data (possibly using various measuring techniques) that can be combined in a single rich dataset. Moreover, laboratory tests on dam-break flows are widely considered a valuable benchmark for the validation of numerical models, since field data from historical events are scarce, sparse, and highly uncertain. However, no systematic review of laboratory investigations of dam-break flows and existing related datasets are available in the literature to provide a comprehensive overview of the test conditions considered, the measuring techniques used, and the experimental data collected. This review article aims to fill this gap, focusing on laboratory tests in schematic and idealized setups with a fixed, non-erodible bed. In particular, this review aims to help researchers and modelers to: (a) select the most appropriate laboratory tests for validating their numerical models; (b) facilitate access to databases by indicating relevant bibliographic references; (c) identify specific challenging aspects worthy of further experimental research; and (d) support the development of new or improved technologies for the mitigation of the impact of dam-break flood waves. The references reviewed are organized into tables according to the purposes of the laboratory investigation, and comprehensive information is provided on test conditions, datasets, and data accessibility. Finally, suggestions for future experimental research on dam-break flows are provided.

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“…However, many traditional data collection systems face challenges such as the high cost and labor required to maintain existing stations and keep up with the rapid needs for creating new data that matches the pace of other changes such as land use change (Muste et al, 2017;Pike et al, 2019;WMO, 2015). Among all prospective developments that may increase data availability in the future, two components stand out, notably, crowd-sourced data and remotely sensed imagery (Mignot & Dewals, 2022;Mishra et al, 2022). Although both can cover a large area rapidly, remotely sensed imagery has a significant advantage in terms of data accuracy and temporal and spatial consistency.…”

Section: Introductionmentioning

confidence: 99%

U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding

Demir

2023

Science of The Total Environment

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Hydraulic modelling of inland urban flooding: Recent advances

Cited by 57 publications

References 108 publications

Improved runoff forecasting based on time-varying model averaging method and deep learning

Improved runoff forecasting based on time-varying model averaging method and deep learning

Review of Experimental Investigations of Dam-Break Flows over Fixed Bottom

U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding

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