Uncertainties of urban flood modeling: Influence of parameters for different underlying surfaces

Liu, Jiahong; Shao, Weiwei; Xiang, Chenyao; Mei, Chao; Li, Zejin

doi:10.1016/j.envres.2019.108929

Cited by 78 publications

(35 citation statements)

References 24 publications

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“…However, there also remain many challenges and barriers that affect the effectiveness and delivery of the SCP, as the program has not worked as well as expected in reducing urban flood disasters (e.g. waterlogging) in many pilot cities (Chan et al, 2018;Jiang et al, 2018;Nguyen et al, 2019;Liu et al, 2020). This inevitably raises questions on the functions and effects of the program.…”

Section: Introductionmentioning

confidence: 99%

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

Sang

Sivakumar

Chan

et al. 2020

Journal of Hydrology

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Section: Introductionmentioning

confidence: 99%

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

Sang

Sivakumar

Chan

et al. 2020

Journal of Hydrology

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“…For the assessment of the qualitative aspect of input data in the hydrodynamic model is in many scientific studies suggested the importance given to the hydrological and hydraulic part [68]. We noticed that the authors such as [69,70] present a consensus view that the input parameters and their uncertainties affect modeling results mainly in the extent of inundation area and water depth.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Selected Sub-Elements of Spatial Data Quality on 3D Flood Event Modeling: Case Study of Prešov City, Slovakia

et al. 2020

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Weather-related disasters represent a major threat to the sustainable development of society. This study focuses directly on the assessment of the state of spatial information quality for the needs of hydrodynamic modeling. Based on the selected procedures and methods designed for the collection and processing of spatial information, the aim of this study was to assess their qualitative level of suitability for 3D flood event modeling in accordance with the Infrastructure for Spatial Information in the European Community (INSPIRE) Directive. In the evaluation process we entered geodetic measurements and the digital relief model 3.5 (DMR 3.5) available for the territory of the Slovak Republic. The result of this study is an assessment of the qualitative analysis on three levels: (i) main channel and surrounding topography data from geodetic measurements; (ii) digital relief model; and (iii) hydrodynamic/hydraulic modeling. The qualitative aspect of the input data shows the sensitivity of a given model to changes in the input data quality condition. The average spatial error in the determination of a point’s position was calculated as 0.017 m of all measured points along a watercourse and its slope foot and slope edge. Although the declared accuracy of DMR 3.5 is assumed to be ±2.50 m, in some of the sections in the selected area there were differences in elevation up to 4.79 m. For this reason, we needed a combination of DMR 3.5 and geodetic measurements to refine the input model for the process of hydrodynamic modeling. The quality of the hydrological data for the monitored N annual flow levels was of fourth-class reliability for the selected area.

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“…This study provides a scenario-based urban flood forecasting method. These urban flood simulation models have achieved good results in urban flood simulation and risk analysis by building different modules, such as runoff yield, confluence, and channels [35], [36], [37]. In addition, with the improvement of computer capabilities and the development of geographic information system (GIS) technology, the spatial resolution of surface data has been significantly improved in recent years, and the division of the model spatial scale has become increasingly detailed [38], [39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Urban floods are the result of the comprehensive effect of climate variables and underlying surface conditions (including rainfall, topography, river network, land use and pipe network) [37], in which rainfall is the driving factor of urban flood [52]. Due to the limited change of the underlying surface conditions in the city in the short term, rainfall is the direct reason of urban flooding [53].…”

Section: Selection Of Sensitivity Index For Depth Prediction Of Acmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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Uncertainties of urban flood modeling: Influence of parameters for different underlying surfaces

Cited by 78 publications

References 24 publications

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

Evaluation of Selected Sub-Elements of Spatial Data Quality on 3D Flood Event Modeling: Case Study of Prešov City, Slovakia

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

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