Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
Input data for LSTM and seq2seq LSTM surrogate models for multi-step-ahead street-scale flood forecasting in Norfolk, VA
Authors: |
|
|
---|---|---|
Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
Type: | Resource | |
Storage: | The size of this resource is 471.7 MB | |
Created: | Aug 30, 2023 at 6:12 p.m. | |
Last updated: | May 28, 2024 at 9:33 p.m. | |
Citation: | See how to cite this resource |
Sharing Status: | Public |
---|---|
Views: | 470 |
Downloads: | 44 |
+1 Votes: | 2 others +1 this |
Comments: | No comments (yet) |
Abstract
This resource includes the input data for LSTM and seq2seq LSTM surrogate models for multi-step-ahead street-scale flood forecasting in Norfolk, VA, USA. The data consists of topographic features: topographic wetness index (TWI), depth to water (DTW) and elevation, and environmental features: hourly rainfall and tide level from gauge stations and water depth generated by the physics-based model TUFLOW.
There are three folders in this resource -
1. The "OriginalData" folder includes the CSV files for the top 20 daily storm events from 2016-2018 for the streets of Norfolk.
2. The "FloodproneStreets" folder includes shapefiles of the street segments (polygons of 7.2 m width x 50 m length) of Norfolk. Alongside, it includes a CSV file containing 22 flood-prone streets selected from the STORM report.
3. The "RelationalDatabase" folder includes three CSV files for node_data (varied spatially), tide_data (varied temporally) and weather_data tide_data (varied spatially and temporally) for efficient data management. The notebook script "create_relational_data.ipynb" is used to convert "OriginalData" to "RelationalDatabase".
The Python script of the LSTM and seq2seq LSTM surrogate models is available on GitHub https://github.com/br3xk/LSTM-and-seq2seq-LSTM-surrogate-models-for-street-scale-flood-forecasting
The output of the model is forecasted hourly water depth on the 22 flood-prone streets with 4-hr and 8-hr lead.
Subject Keywords
Coverage
Spatial
Temporal
Start Date: | |
---|---|
End Date: |
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/![CC-BY](https://storage.googleapis.com/hydroshare-prod-static-media/static/img/cc-badges/CC-BY.04d377d96855.png)
Comments
There are currently no comments
New Comment