From Cloud to Refugee Camp: A Satellite-Based Flood Analytics Case-Study in Congo-Brazzaville

Ho, Jeff C.; Vu, William; Tellman, Beth; Dinga, Jean Bienvenu; N’diaye, Patrick Impeti; Weber, Sam; Bauer, Jean-Martin; Schwarz, Bessie; Doyle, Colin; Demuzere, Matthias; Anderson, Tyler; Glinskis, E.

doi:10.1016/b978-0-12-819412-6.00006-7

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Radar can detect surface water even when clouds are present (Zhao et al, 2021) while optical sensors image the earth daily at 5-500m resolutions. Earth observations of flood inundation improve disaster response (Schumann et al, 2018), rapid aid assessment and financing from assistance relief programs (Ho et al, 2021), and access to financial recovery through insurance (Tellman et al, 2022). The International Charter: Space and Major Disasters (https://disasterscharter.org; accessed November 2023) enables governments and satellite providers to rapidly map floods and share data for major global events to improve flood response.…”

Section: Satellite Observations Are a Powerful But Incomplete Tool To...mentioning

confidence: 99%

Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network

Frame,

Nair,

Sunkara

et al. 2024

Preprint

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Rapid and accurate maps of floods across large domains, with high temporal resolution capturing event peaks, have applications for flood forecasting and resilience, damage assessment, and parametric insurance. Satellite imagery produces incomplete observations spatially and temporally, and hydrodynamic models require tradeoffs between computational efficiency and accuracy. We address these challenges with a novel flood model which predicts surface water area from the U.S. National Water Model using a convolutional neural network (NWM-CNN). We trained NWM-CNN on 780 flood events, at a 250m resolution with an RMSE of 4.58% on held out validation geographies. We demonstrate NWM-CNN across California during the 2023 atmospheric rivers, comparing predictions against Sentinel-1 mapped flood observations. Historically, we compared the data from 1979-2023 to flood damage reports in Sacramento County, California. Results show that NWM-CNN captures inundation extent better than the Height Above Nearest Drainage (HAND) approach (25% to 36% RMSE, respectively).

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Section: Satellite Observations Are a Powerful But Incomplete Tool To...mentioning

confidence: 99%

Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network

Frame,

Nair,

Sunkara

et al. 2024

Preprint

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“…Pluvial flood risk reduction measures may include improved camp drainage, retrofitting of structures, nature-based solutions, or early warning systems [59]. In camps where the risk from fluvial flooding (such as in Okugo or Kule) is significant, this information could be used to relocate refugees from high-risk areas (see for example Ho, Vu [60]) or inform the construction of structural adaptation measures.…”

Section: Refugee Camp Flood Riskmentioning

confidence: 99%

Unknown risk: assessing refugee camp flood risk in Ethiopia

Bernhofen,

Blenkin,

Trigg

2023

Environ. Res. Lett.

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The number of global refugees has been rising annually for the last decade. Many of these refugees are housed within camps, in temporary structures, vulnerable to the impacts of flooding. The flood risk of refugees is not well understood. Flood risk guidance available for camp planners and managers is vague, and existing flood risk data is often lacking in the remote areas where camps are typically located. We show how global data should, and should not, be used to assess refugee flood risk in Ethiopia; a country hosting 725,000 refugees, primarily from 4 neighbouring countries, in 24 camps. We find that global population datasets, typically used in national flood risk assessments, do not accurately capture camp populations. Even the most accurate global population datasets are missing three fifths of camp flood exposure. We propose, and test, alternative approaches for representing exposure that combine reported estimates of camp population with data on camp area, building footprints, and population density. Applying these approaches in our national flood risk assessment, we find that 95.8% of camps in Ethiopia are exposed to flooding of some degree and between 143,208 (19.8%) and 182,125 refugees (25.2%) are exposed to a 1% Annual Exceedance Probability flood (100-year return period). South Sudanese refugees are the nationality most exposed to flooding, but Eritrean refugees are the nationality most exposed to flooding with a high risk to life. Promisingly, we find that many camps may be set up in such a way that reduces the exposure of refugees to flooding. Our study demonstrates that global data, augmented with local data, can be useful for understanding the flood risk of refugee camps. The consistent scalable approach can be used as a first-order analysis of risk, identifying risk hotspots, and helping to prioritize further detailed analyses to inform within-camp adaptation.

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“…These techniques have been known to misclassify (overestimate) water bodies (Khalid et al., 2021). Machine learning methods, such as Support Vector Machine classifiers and Random Forest algorithms have also been adopted in several floodplain mapping studies (e.g., Mobley et al., 2021; Nandi et al., 2017) with significant contributions to near real‐time flood hazard mapping (Ho et al., 2021). Big data analytics and computer vision techniques (specifically, deep learning) are now paving the way for automated delineation of flood extents with high accuracy (J. Wang et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Satellite Video Remote Sensing for Flood Model Validation

Masafu,

Williams

2024

Water Resources Research

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Satellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

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From Cloud to Refugee Camp: A Satellite-Based Flood Analytics Case-Study in Congo-Brazzaville

Cited by 4 publications

References 11 publications

Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network

Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network

Unknown risk: assessing refugee camp flood risk in Ethiopia

Satellite Video Remote Sensing for Flood Model Validation

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