Large‐scale high‐resolution pluvial flood hazard mapping using the raster‐based hydrodynamic two‐dimensional model <scp>FloodAreaHPC</scp>

Tyrna, Bernd; Assmann, André; Fritsch, Kathrina; Johann, Georg

doi:10.1111/jfr3.12287

Cited by 30 publications

(26 citation statements)

References 40 publications

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“…The generated results by geological-geomorphological methods are also more reasonable than the 1D hydrological-hydraulic methods when river channel is changeable over time and has a high erosive potential [19]. In this regard, some studies have been conducted on flood modeling using hydrological-hydraulic methods including Horritt et al [21], Di Baldassarre et al [22], Grimaldi et al [23], Nguyen et al [20], and Tyrna et al [26].…”

Section: Introductionmentioning

confidence: 93%

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

Bui

Khosravi

et al. 2018

Water

197

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This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

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Section: Introductionmentioning

confidence: 93%

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

Bui

Khosravi

et al. 2018

Water

197

View full text Add to dashboard Cite

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“…The application of hydraulic model in flood hazard assessment is becoming popular. We can mention the studies by Afifi et al (2019), Zin et al (2018), and Tyrna et al (2018) that applied different hydraulic models in studying flood hazard. At the same time, land use-based and communitybased flood vulnerability study is also getting prioritised.…”

Section: Introductionmentioning

confidence: 99%

Flood Hazard, Vulnerability and Risk Assessment for Different Land Use Classes Using a Flow Model

2019

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This study is concerned with flood risk that can be assessed by integrating GIS, hydraulic modelling and required field information. A critical point in flood risk assessment is that while flood hazard is the same for a given area in terms of intensity, the risk could be different depending on a set of conditions (flood vulnerability). Clearly, risk is a function of hazard and vulnerability. This study aims to introducing a new approach of assessing flood risk, which successfully addresses this above-mentioned critical issue. The flood risk was assessed from flood hazard and vulnerability indices. Two-dimensional flood flow simulation was performed with Delft3D model to compute floodplain inundation depths for hazard assessment. For the purpose of flood vulnerability assessment, elements at risk and flood damage functions were identified and assessed, respectively. Then, finally flood risk was assessed first by combining replacement values assessed for the elements and then using the depth-damage function. Applying this approach, the study finds that areas with different levels of flood risk do not always increase with the increase in return period of flood. However, inundated areas with different levels of flood depth always increase with the increase in return period of flood. The approach for flood risk assessment adopted in this study successfully addresses the critical point in flood risk study, where flood risk can be varied even after there is no change in flood hazard intensity.

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“…Here simulation techniques are an important tool to foresee the results of an action and assist the decision-making process. Particularly hydraulic models are powerful tools, if essential data of the failing levee and its surroundings is available (Tyrna et al, 2018). In the hydrodynamic simulation as well as in the real process, the conditions in the immediate vicinity of the levee failure have the highest influence on the progressing flood.…”

Section: Manuscriptmentioning

confidence: 99%

Surface Flow Velocity Measurements From Uav-Based Videos

Brauneck

Gattung

Jüpner

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Most measuring methods for determining the volumetric flow rate or surface flow velocity have in common that they cannot be safely used under extreme outflow conditions. Especially in catastrophic situations, it is of particular interest to determine the amount of water that flows into the hinterland as precisely as possible in order to improve hydrodynamic models. Faulty assumptions lead to misleading calculations and may result in preventable casualties. As technical improvements throughout the last decade facilitated the widespread utilization of unmanned aerial vehicles (UAV) or remotely piloted aircraft systems (RPAS), these systems are now capable to collect and transmit precise information from remote areas to task forces immediately. The usage of a UAV is possible with minimal preparation at almost every place and is suitable for improving the database for a quick assessment of the status during a catastrophic event.In this work, the determination of surface flow velocity using unmanned aerial vehicles (UAV) and floating optical tracers is evaluated. It is also discussed, to what extent numerical methods are able to efficiently undistort and correct this data. Precision analysis of video data from field investigations was performed with R using three different approaches that calculated the true velocity of the floating objects. The results indicate similar degrees of precision for both advanced methods but calculating an ortho-corrected video is a timeconsuming process not suitable for nearly real-time applications.

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Large‐scale high‐resolution pluvial flood hazard mapping using the raster‐based hydrodynamic two‐dimensional model FloodAreaHPC

Cited by 30 publications

References 40 publications

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

Flood Hazard, Vulnerability and Risk Assessment for Different Land Use Classes Using a Flow Model

Surface Flow Velocity Measurements From Uav-Based Videos

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