1537 19 6069 2 33069 document 6069 ReadMe_1537.txt text/plain fbf220d8e762c65aec7f3c2081cf4c53 MD5 2684 2023-11-20 11:30:19 https://researchdata.gla.ac.uk/1537/1/ReadMe_1537.txt 1537 1 1 text/plain Text en public cc_by_4

ReadMe_1537.txt

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indexcodes.txt

http://eprints.org/relation/isVersionOf https://researchdata.gla.ac.uk/id/document/6069 http://eprints.org/relation/isVolatileVersionOf https://researchdata.gla.ac.uk/id/document/6069 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.gla.ac.uk/id/document/6069 6073 2 33085 document 6073 Dataset_1537.zip application/zip e3e44283510f26b7e7aade928ade5be3 MD5 2039848687 2023-11-20 12:02:22 https://researchdata.gla.ac.uk/1537/3/Dataset_1537.zip 1537 3 3 application/zip Mixed en public cc_by_4

Dataset_1537.zip

data archive 6975 disk0/00/00/15/37 2023-11-20 12:10:57 2024-03-18 12:36:29 2023-11-20 12:10:57 data_collection show Masafu Christopher Kusimba 52326 0000-0002-2674-1507 Williams Richard 34783 0000-0001-6067-1947 glaresearchdata:2023-11-20-1537 pub 30400000 This work was funded by a Lord Kelvin Adam Smith PhD studentship This repository contains data and scripts for an investigation that used satellite video remote sensing to validate a hydraulic model of the Darling River at Tilpa, Murray-Darling basin, Australia. It encompasses a range of materials, including satellite video footage of a flood (acquired 5 February 2022, 23:12 UTC), data and results from HEC-RAS 2D model simulations of the flood, as well as flood extents derived through the use of deep learning techniques. The Python script used for deep learning is included (for practical considerations, users will need Python 3.x and PyTorch 2.x, alongside other specific libraries, for running the deep learning script, details of which are described in the script itself). 2023-11-20 published University of Glasgow 10.5525/gla.researchdata.1537 Mixed University of Glasgow FALSE English 2022-02 2022-02 2033-11-20 R file_share FALSE FALSE https://eprints.gla.ac.uk/id/eprint/309906