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7 results for “Flood processes”
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Spatial flood events and process types
Spatially clustered flood events from peak flows identified at selected GRDC sations. The data also shows the stations affected by each of the clustered eevents. The clustered events were classified into four different process types. Furthermore, five regions were defined across Europe and the regions affected by each flood event are identified. Further description and content of each data file can be seen in the included metadata.
Process Controls on Flood Seasonality in Brazil - link to data and plots
<p>This data set and plots of circular histograms accompany the paper "Process Controls on Flood Seasonality in Brazil" at Geophysical Research Letters (<a href="https://doi.org/10.1029/2021GL096754">https://doi.org/10.1029/2021GL096754</a>). In this paper, we investigate the relationship between the seasonality of floods, maximum annual rainfall, and maximum annual soil moisture data of 886 basins in Brazil for 1980-2015 to shed light on process controls of flood generation.</p>
Comprehensive Automatic Processing and Analysis of Adaptive Optics Flood Illumination Retinal Images
<p>A collaborative research group has established this database to support AO-FIO image utilization and evaluation of photoreceptor detection. <br> Please cite the following publication when using the database:</p> <p>Eva Valterova, Jan D. Unterlauft, Mike Francke, Toralf Kirsten, Radim Kolar, and Franziska G. Rauscher, "Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects," Biomed. Opt. Express <strong>14</strong>, 945-970 (2023)<br> <br> The database can be utilized in connection with our application MATADOR for AO-FIO image registration and analysis, which is freely available on:</p> <p>https://github.com/evavalterova/MATADOR.git</p> <p>The database includes</p> <ul> <li>over 200 flood illumination adaptive optics images of 10 normal healthy subjects. Each folder includes 10 images of the right eye (denoted by OD) and 10 images of the left eye (denoted by OS) with their preliminary determined retinal position during image acquisition.</li> <li>foveal and peripheral patches. Each consists of 40 cropped regions from the set of 200 images. In each cropped region are manually labeled positions of photoreceptors by three evaluators.</li> <li>axial lengths of 10 subjects in ".xlsx" file<br> </li> </ul>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Processed data: flood generation processes and flood anomalies
<p>This dataset inludes processed data required to reproduce main results presented in the manuscript by Tarasova et al. "Shifts in flood generation processes exacerbate regional flood anomalies in Europe".</p>
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