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A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

Han Wang, Yunqing Xuan Orcid Logo

Remote Sensing, Volume: 14, Issue: 15, Start page: 3823

Swansea University Authors: Han Wang, Yunqing Xuan Orcid Logo

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DOI (Published version): 10.3390/rs14153823

Abstract

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal ave...

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Published in: Remote Sensing
ISSN: 2072-4292
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60784
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spelling 2022-08-24T12:44:30.4705561 v2 60784 2022-08-09 A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets a718b31e3f1749890106d990af40fb3c Han Wang Han Wang true false 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2022-08-09 FGSEN This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included. Journal Article Remote Sensing 14 15 3823 MDPI AG 2072-4292 spatiotemporal pattern; pattern recognition; cluster analysis; machine learning; pattern identification and classification; rainfall monitoring and nowcasting 8 8 2022 2022-08-08 10.3390/rs14153823 Data Availability Statement: The datasets for testing the toolbox were provided by the Centre ofHydrology and Ecology (CEH) and available in https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca (accessed on 4 August 2022), and the details of the catchments can be found inhttps://nrfa.ceh.ac.uk/content/catchment-boundary-and-areas . COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This research was funded by the Academy of Medical Sciences, Grant Ref: GCRFNGR4_1165. The Guangdong Radar data were provided by the UK-China joint project “Early Warning Systems for Urban Flooding in Chinese Mega Cities using Advanced Phased Array Radar” supported by the Royal Academic of Engineering, Grant Ref: UUFRIP-10021. 2022-08-24T12:44:30.4705561 2022-08-09T18:23:35.6343995 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Han Wang 1 Yunqing Xuan 0000-0003-2736-8625 2 60784__24903__3af5d4a1ba3f4a479def2a0d638e1b7f.pdf remotesensing-14-03823-v2.pdf 2022-08-09T18:26:30.1508852 Output 8055692 application/pdf Version of Record true © 2022 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
spellingShingle A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
Han Wang
Yunqing Xuan
title_short A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
title_full A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
title_fullStr A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
title_full_unstemmed A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
title_sort A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets
author_id_str_mv a718b31e3f1749890106d990af40fb3c
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv a718b31e3f1749890106d990af40fb3c_***_Han Wang
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Han Wang
Yunqing Xuan
author2 Han Wang
Yunqing Xuan
format Journal article
container_title Remote Sensing
container_volume 14
container_issue 15
container_start_page 3823
publishDate 2022
institution Swansea University
issn 2072-4292
doi_str_mv 10.3390/rs14153823
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included.
published_date 2022-08-08T04:19:11Z
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score 10.999161

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