1 to 10 of 54 Results
Mobley, William, 2022, "Output for Probability of Rescue Requests from Twitter", https://doi.org/10.18738/T8/OOJL5D, Texas Data Repository, V1
Rescue requests during large-scale urban flood disasters can be difficult to validate and prioritize. High-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurrin... |
William Mobley, 2020, "Flood Hazard Modeling Output", https://doi.org/10.18738/T8/FVJFSW, Texas Data Repository, V3
The results from a flood hazard study using the Random Forest Classification method to predict the probability of flooding at 30-m resolution for a 30,523 km2 area. We generate flood hazard maps for twelve USGS 8-digit watersheds along the coast in southeast Texas. |
Aug 11, 2021 - Sreyoshi Patra Dataverse
Patra, Sreyoshi; Beyerlein, Michael, 2021, "Competencies of OD Professionals", https://doi.org/10.18738/T8/BNLLTF, Texas Data Repository, V1, UNF:6:lFHVdeb8GZLH0u5uDbdr1Q== [fileUNF]
This dataverse contains data related to the research on competencies of Organization Development (OD) professionals. |
Aug 11, 2021
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William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Impervious", https://doi.org/10.18738/T8/S7NFPI, Texas Data Repository, V2
Percent impervious was measured using the percent developed impervious surface raster from the National Land Cover Database (NLCD). |
Jun 16, 2021 - Measuring, Mapping, and Managing Flood Risk in Texas
William Mobley, 2021, "Flood Hazard Modeling: Structural Output", https://doi.org/10.18738/T8/IPWHEL, Texas Data Repository, V1
The results from a flood hazard study using the Random Forest Classification method to predict the probability of flooding at 30-m resolution for a 30,523 km2 area. The shapefile contains structures within the study area. Two columns are available Flood Probability represents the... |
May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas
William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, TWI", https://doi.org/10.18738/T8/85LBLA, Texas Data Repository, V1
TWI is calculated by the following equation: TWI= Ln ((flow_accumulation * 900) + 1 )/(Tan((slope*π)/180 )) Where high values of TWI are associated with areas that are concave, low gradient areas where water often accumulates and pools making them more vulnerable to floods. |
May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas
William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Roughness", https://doi.org/10.18738/T8/5ZQMXV, Texas Data Repository, V1
Roughness values were assigned to each NLCD land cover class using the values suggested by Kalyanapu (2009), and, like KSAT, was averaged across the contributing upstream area for each raster cell for 2016. Kalyanapu, A.J., Burian, S.J. & McPherson, T.N., 2010. Effect of land use... |
May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas
William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Distance to Coast", https://doi.org/10.18738/T8/YYWO9P, Texas Data Repository, V1
Distance to coast were calculated using Euclidean Distance based on the National Hydrography Dataset (NHD) stream and coastline features. |
May 4, 2020 - Measuring, Mapping, and Managing Flood Risk in Texas
William Mobley, 2020, "Replication Data for: Flood Hazard Modeling, Average Ksat", https://doi.org/10.18738/T8/UKAGX0, Texas Data Repository, V1
KSAT values were assigned to soil classes obtained from the Natural Resources Conservation Service’s (NRCS) Soil Service Geographic Database (SSURGO) , and then averaged across the upstream area for each cell. (2020-04-28) |
