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.
| 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 169.0 MB | |
| Created: | Oct 26, 2023 at 3:58 a.m. | |
| Last updated: | Apr 29, 2024 at 12:55 p.m. | |
| DOI: | 10.4211/hs.0b994c0f13f445ababaa8858ece6e843 | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content Geographic Raster Content |
| Sharing Status: | Published |
|---|---|
| Views: | 113 |
| Downloads: | 1 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
The first part of this repository includes a Python file containing two functions that utilize ESRI's ArcGIS arcpy library. Users can input shapefiles (polygons) of sub-catchments and raster files of land use land cover, and soil types. Additionally, another function allows users to input shapefiles (polylines) of the stormwater network, which include data on built material types and the age of infrastructure, to generate grouped categories of sub-catchments and stormwater conduits.
The second part of the repository contains an R file, which includes two algorithms. The first algorithm extracts time series data of nodes' flooding from a one-dimensional SWMM model and overland flood water depth from a one- and two-dimensional coupled version of the SWMM model. It then establishes a statistical relationship between the two models. The second algorithm parameterizes the SWMM 1D version using a "Genetic Algorithm" for single objective optimization in parallel computing nodes.
For details about this work readers are referred to:
1). Shrestha, A., Garcia, M. & Doerry, E. (2024). Leveraging catchment scale automated novel data collection infrastructure to advance urban hydrologic-hydraulic modeling. Environmental Modelling & Software.
https://doi.org/10.1016/j.envsoft.2024.106046
2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y
For GitHub links to this repository, and any updates, readers are referred to:
1). https://github.com/ashish-shrs/Parameter_grouping_for_hydrologic-hydraulic_model_calibration
2). https://github.com/ashish-shrs/Algorithm_for_novel_data_application_in_hydrologic-hydraulic_model_calibration
Subject Keywords
Coverage
Spatial
Content
Data Services
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | 1831475 |
How to Cite
This resource is shared under the Creative Commons Attribution-NoCommercial-ShareAlike CC BY-NC-SA.
http://creativecommons.org/licenses/by-nc-sa/4.0/
Comments
There are currently no comments
New Comment