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LSTM Efficacy in Runoff Prediction: A Study Using Spatial Datasets Across Diverse Meteorological Conditions Including Big Sandy River.
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| Type: | Resource | |
| Storage: | The size of this resource is 29.8 MB | |
| Created: | Mar 20, 2024 at 1:46 a.m. | |
| Last updated: | Mar 20, 2024 at 7 a.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content Geographic Raster Content |
| Sharing Status: | Public |
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| Views: | 111 |
| Downloads: | 6 |
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Abstract
This resource comprises various files pertaining to time series data, particularly focusing on NWM (National Water Model) short-range forecast and USGS observations of streamflow data for three stations, measured in cubic feet per second (cfs). I added some spatial datasets in the form of vector and raster datasets just for one specific research area.
The contents of each file serve distinct purposes:
- "USGS Observation and NWM Outputs" is consisted of merged NWM forecast and USGS observation data;
-"Data types" highlights some information including coordinates and reach ID and gage ID for specific locations in Arizona, Nevada, and Wisconsin in the USA;
- "Results" showcases images associated with the statistical metrics for aforementioned locations, offering visual insights into data analysis outcomes;
-"Data Collection and Analysis" summarizes merged data from the NWM and USGS, accompanied by statistical metrics for analysis;
- "LSTM Paper" presents an incomplete paper on LSTM models application to the dataset, necessitating revision and completion in the near future;
-"Big Sandy watershed " includes Vector data (shapefiles) for the delineated watershed shapefiles.
-"Big Sandy streamlines" is consist of the stream lines for the specific watershed.
-"Big Sandy River " includes the raster data for the delineated watershed which contains big sandy river.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Data Services
Additional Metadata
| Name | Value |
|---|---|
| Gage Type | River gage |
| Spatial data | GCS_WGS_1984 projection |
| NWM Forecasts | Time series data |
| Analysis Periods | April Month |
| USGS Observation Streamflow | Time series data |
Related Resources
| This resource is described by | Han, H., Morrison, R. R., (2022). Improved runoff forecasting performance through error predictions using a deep-learning approach. Journal of Hydrology (Elsevier) https://doi.org/10.1016/j.jhydrol.2022.127653 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Cooperative Institute for Research to Operations in Hydrology (CIROH) | Collaborative Research: Advancing Data Scienceand Analytics for Flood Predictions |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
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| Dan Ames | Brigham Young University | Utah, US | (801) 422-3620 | ResearchGateID , GoogleScholarID |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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