River Flood Modeling and Remote Sensing Across Scales: Lessons from Brazil

Fleischmann, Ayan Santos; Brêda, João Paulo Fialho; Rudorff, Conrado M.; Paiva, Rodrigo Cauduro Dias de; Collischonn, Walter; Papa, Fabrice; Ravanello, Mariane Moreira

doi:10.1016/b978-0-12-819412-6.00004-3

Cited by 6 publications

(6 citation statements)

References 112 publications

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“…The development of remote sensing technology, especially Interferometric Synthetic Aperture Radar (InSAR), is increasingly used to determine flood inundation areas in specific flood events (Fleischmann et al., 2021; Refice et al., 2014). However, susceptible areas for future flood occurrences still cannot be identified by sole use of InSAR and other remote sensing approaches.…”

Section: Introductionmentioning

confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

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Section: Introductionmentioning

confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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“…Thus, despite being natural events, the uncontrolled development in river plains increases the risks and socioeconomic damages caused by floods, which, in general, result in loss of life (Martin-Díaz et al, 2018;Cobbinah et al, 2022). Due to Brazil's climate and historically uncontrolled development of flood-prone areas (Fleischmann et al, 2021), several millions reais are spent annually to combat the adverse effects of flooding catastrophes (Bitencourt and Rocha, 2014).…”

Section: Introductionmentioning

confidence: 99%

Methodological proposal to remote detection and management of areas that are naturally vulnerable to floods

Servidoni

Ayer²,

Lense

et al. 2023

Earth sci. res. j.

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Floods are the main natural disasters in Brazil, causing loss of life and socioeconomic damage. This work proposes a model for the remote detection of areas that are naturally flood-prone due to the morphometric characteristics of their relief and drainage networks in the Alto Sapucaí River in Minas Gerais, Brazil. The morphometric parameters used were the drainage density, river density, relief ratio, roughness index, maintenance coefficient, form factor and stream surface length. The risk areas had a compactness coefficient of 0.75 and a form factor of 0.56, and both were considered a high risk for floods. The obtained results allowed the identification of a significant predictive equation that suggested a cutoff value of 3.82 for the discriminant function; areas with values under this cutoff were considered naturally more vulnerable to floods occurrences. These areas were corroborated with the emergency maps of the municipalities. The map obtained by the proposed model was compared with the Civil Defense map, and its accuracy, according to the Kappa coefficient, was 0.83, indicating strong similarity between the two maps.

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“…The hydrodynamic simulation represents a more complex technique and apply mathematical equations for modeling the flood wave propagation and the associated flooded area (Alcrudo, 2004;Lauriano et al, 2011;Ahmad et al, 2016;Fleischmann et al, 2021). There are several software that can be applied to estimate the flood extent.…”

Section: Introductionmentioning

confidence: 99%

“…These models can provide flood hazard maps estimate that are recognized in the literature as more precise than large scale models (Fleischmann et al, 2021). However, they are computationally heavy and need several data from cross sections that are difficult to obtain when applications are focused on large scales.…”

Section: Introductionmentioning

confidence: 99%

Assessing the capacity of large-scale hydrologic-hydrodynamic models for mapping flood hazard in southern Brazil

Alves

Fan

Paiva

et al. 2022

RBRH

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Mapping flood risk areas is important for disaster management at the local, regional, and national scales. The aim of this study was to evaluate the ability of large-scale models to obtain flood hazard maps. The models were compared to the estimates developed by the Brazilian Geological Survey (CPRM) for different return periods (RP). The floods were evaluated for the municipalities of Uruguaiana, Montenegro and São Sebastião do Caí in the Rio Grande do Sul state. It was shown that the flood mapping generated by MGB covers larger areas (greater than 1000 km2; Siqueira et al. 2018), with a lower cost of obtaining for large scales. The - Hit Rate of the regional and continental MGB model versions with the CPRM maps ranged from about 40% to 90% in different cities, and the Hit Rate between the regional model and the CPRM map increased with the increased return period floods. The continental model compatibility was similar for all analyzed RPs. Our results suggest the agreement in terms of Hit Rate of current large-scale hydrological-hydrodynamic models to assess flood hazard.

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River Flood Modeling and Remote Sensing Across Scales: Lessons from Brazil

Cited by 6 publications

References 112 publications

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Methodological proposal to remote detection and management of areas that are naturally vulnerable to floods

Assessing the capacity of large-scale hydrologic-hydrodynamic models for mapping flood hazard in southern Brazil

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