Two-dimensional numerical models for overland flow simulations

Costabile, Pierfranco; Costanzo, Carmelina; Macchione, Francesco

doi:10.2495/rm090131

Cited by 17 publications

(15 citation statements)

References 18 publications

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“…Since excess rainfall can cause flooding and mudslides which has the potential to cause loss of human life and disrupt the local economy, reliably predicting the flow variables for different precipitation scenarios can assist decision-makers and emergency personnel. Researchers have modeled these flows by solving the two-dimensional fully dynamic shall water Equations [10,11] or their diffusion [12,13] or kinematic approximations [14,15].…”

Section: Two-dimensional Applications 31 Overland Flow On a Sloping mentioning

confidence: 99%

Comparison of DHM Results for One- and Two-Dimensional Flows with Experimental and Numerical Data

Hromadka¹,

Rao²

2020

A Diffusion Hydrodynamic Model

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In this chapter, the performance of DHM for one-and two-dimensional flows is compared with the results of HEC-RAS, HEC-RAS 2D, WSPG, TUFLOW, Mike 21, and OpenFOAM models. The latter four models are currently widely used in industry, and benchmarking their data with DHM can shed more light on the reliability of DHM. As the results indicate, for applications which do not violate the assumptions made in DHM, the results are in agreement.

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Section: Two-dimensional Applications 31 Overland Flow On a Sloping mentioning

confidence: 99%

Comparison of DHM Results for One- and Two-Dimensional Flows with Experimental and Numerical Data

Hromadka¹,

Rao²

2020

A Diffusion Hydrodynamic Model

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“…The surface integrals represent the conversion of the lineal integral into area integrals implying to contemplate the boundary of the region that involves some integrations (Green's theorem). Some schemes consider the homogeneous conservative part of the system and a discretisation of the non-conservative term with a "lateralisation" [25]. For that, it is necessary to take into account that the mesh could have known depth and velocity values and the border boundaries consider four points: one for the right side of the intercell (R) and another for the left side (L) and the other two for above and below the cell.…”

Section: Flubidi Mathematical Modelmentioning

confidence: 99%

Flood Risk Assessment in Housing under an Urban Development Scheme Simulating Water Flow in Plains

Cruz¹,

Mariles²,

Hernández³

et al. 2019

Recent Advances in Flood Risk Management

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Floods are increasingly occurring around the world more often, this implies analysing the risks connected to both human health and the environment, and to infrastructure and properties. The objective is to establish areas susceptible to flooding and their impact on the population through the effects on the unit of analysis "housing". To simulate the floods and map the affected areas, the FluBiDi 2D model was used. Two conditions for one urban zone analysed within the Mexico Valley were compared: (a) with the current hydraulic infrastructure and (b) with the application of rectification of channels. The available information was the discharge getting into the catchment and the total of homes in 2015. Projections for 20-year and 50-year planning horizon were considered, and for the 50 years, an evaluation of a non-structural measure was applied. Results show that under the current infrastructure, the flood simulated had a flow depth of 20 cm, decreasing to 5 cm average with rectification of channels, and a decrement of 45% of the cost of housing risk. Applying the both structural and non-structural measures, the cost of vulnerable housing was reduced until 94%, thus, this a trustworthy tool for decision-making in urban developments.

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“…A physical model consists of mathematical equations used to describe the physical behavior and interactions of the multiple components involved in a process. Various physical models have been developed for predicting rainfall [5] and surface water flow [6][7][8]. Further, a comprehensive physical model for coastal flooding using parallel computing was developed [9].…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction and Uncertainty Estimation Using Deep Learning

Gude

Corns

Long

2020

Water

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Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems.

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Two-dimensional numerical models for overland flow simulations

Cited by 17 publications

References 18 publications

Comparison of DHM Results for One- and Two-Dimensional Flows with Experimental and Numerical Data

Comparison of DHM Results for One- and Two-Dimensional Flows with Experimental and Numerical Data

Flood Risk Assessment in Housing under an Urban Development Scheme Simulating Water Flow in Plains

Flood Prediction and Uncertainty Estimation Using Deep Learning

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