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1 to 4 of 4 Results

Two-scale aeolian ripples Oct 19, 2023 Yizhaq, Hezi, 2023, "Two-scale aeolian ripples", https://doi.org/10.18738/T8/XA2LNX, Texas Data Repository, V1 Pictures of ripples generated in wind tunnel experiments at Ben-Gurion University, using fine sand (d = 90microns).
Time Series Data and Preprocessing Code for Coastal Flooding Probabilistic Analysis Aug 18, 2023 Kang, Byungho, 2023, "Time Series Data and Preprocessing Code for Coastal Flooding Probabilistic Analysis", https://doi.org/10.18738/T8/CDBYNN, Texas Data Repository, V1, UNF:6:jxXJSCUh3umjOBXK/Z1f0Q== [fileUNF] The time series data, obtained through CNN prediction, along with the associated preprocessing code, are detailed in "Stochastic Properties of Coastal Flooding Events – Part 2: Probabilistic Analysis" (https://doi.org/10.5194/egusphere-2023-238).
Trained CNN, Coastal Imagery Dataset, and Manual Labels for Flooding Detection Aug 18, 2023 Kang, Byungho, 2023, "Trained CNN, Coastal Imagery Dataset, and Manual Labels for Flooding Detection", https://doi.org/10.18738/T8/UYZUBK, Texas Data Repository, V1 The data set encompasses the trained CNN based on ResNet-18, along with the image dataset and the manual labels, as described in "Stochastic Properties of Coastal Flooding Events – Part 1: CNN-based Semantic Segmentation for Water Detection" (https://doi.org/10.5194/egusphere-202...
High water events data Dec 8, 2020 Ramakrishnan, Kiran Adhithya; Rinaldo, Tobia, 2020, "High water events data", https://doi.org/10.18738/T8/HVKLND, Texas Data Repository, V1 The dataset contains time series of total water levels at 12 beaches around the globe. It was obtained using the empirical formulation by Stockton et al (2006, 2014) from publicly available wave and tidal data, as well as digital elevation models. For sites in the US, tidal level...

Two-scale aeolian ripples

Oct 19, 2023

Yizhaq, Hezi, 2023, "Two-scale aeolian ripples", https://doi.org/10.18738/T8/XA2LNX, Texas Data Repository, V1

Pictures of ripples generated in wind tunnel experiments at Ben-Gurion University, using fine sand (d = 90microns).

Time Series Data and Preprocessing Code for Coastal Flooding Probabilistic Analysis

Aug 18, 2023

Kang, Byungho, 2023, "Time Series Data and Preprocessing Code for Coastal Flooding Probabilistic Analysis", https://doi.org/10.18738/T8/CDBYNN, Texas Data Repository, V1, UNF:6:jxXJSCUh3umjOBXK/Z1f0Q== [fileUNF]

The time series data, obtained through CNN prediction, along with the associated preprocessing code, are detailed in "Stochastic Properties of Coastal Flooding Events – Part 2: Probabilistic Analysis" (https://doi.org/10.5194/egusphere-2023-238).

Trained CNN, Coastal Imagery Dataset, and Manual Labels for Flooding Detection

Aug 18, 2023

Kang, Byungho, 2023, "Trained CNN, Coastal Imagery Dataset, and Manual Labels for Flooding Detection", https://doi.org/10.18738/T8/UYZUBK, Texas Data Repository, V1

The data set encompasses the trained CNN based on ResNet-18, along with the image dataset and the manual labels, as described in "Stochastic Properties of Coastal Flooding Events – Part 1: CNN-based Semantic Segmentation for Water Detection" (https://doi.org/10.5194/egusphere-202...

High water events data

Dec 8, 2020

Ramakrishnan, Kiran Adhithya; Rinaldo, Tobia, 2020, "High water events data", https://doi.org/10.18738/T8/HVKLND, Texas Data Repository, V1

The dataset contains time series of total water levels at 12 beaches around the globe. It was obtained using the empirical formulation by Stockton et al (2006, 2014) from publicly available wave and tidal data, as well as digital elevation models. For sites in the US, tidal level...

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