Application of integrated artificial intelligence geographical information system in managing water resources: A review

Climate change has exacerbated the water resources situation by producing erratic rainfall patterns, fading away ice sheets, increasing sea levels, floods, and droughts. Rising temperatures affect precipitation patterns and the entire water cycle, exacerbating water scarcity and water-related danger...

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書誌詳細
出版年:Remote Sensing Applications: Society and Environment
第一著者: 2-s2.0-85193004491
フォーマット: Review
言語:English
出版事項: Elsevier B.V. 2024
オンライン・アクセス:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193004491&doi=10.1016%2fj.rsase.2024.101236&partnerID=40&md5=76ffb6b31b0c75a3a91ed5872b0495e7
id Sapitang M.; Dullah H.; Latif S.D.; Ng J.L.; Huang Y.F.; Malek M.B.A.; Elshafie A.; Ahmed A.N.
spelling Sapitang M.; Dullah H.; Latif S.D.; Ng J.L.; Huang Y.F.; Malek M.B.A.; Elshafie A.; Ahmed A.N.
2-s2.0-85193004491
Application of integrated artificial intelligence geographical information system in managing water resources: A review
2024
Remote Sensing Applications: Society and Environment
35

10.1016/j.rsase.2024.101236
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193004491&doi=10.1016%2fj.rsase.2024.101236&partnerID=40&md5=76ffb6b31b0c75a3a91ed5872b0495e7
Climate change has exacerbated the water resources situation by producing erratic rainfall patterns, fading away ice sheets, increasing sea levels, floods, and droughts. Rising temperatures affect precipitation patterns and the entire water cycle, exacerbating water scarcity and water-related dangers including floods and droughts. As a result, modelling these water resources conundrums based on the various hydrological and meteorological variables has been challenging to ensure effective water management. The target subject of this reviewed study concerns the forecasting of water resources using artificial intelligence and/or geographical information systems, which can be useful in addressing the challenges mentioned. This study presents a few methodologies that have been proposed for modelling the processes that eventually are related to water resources availability, in 70 scientific publications published between 2019 and 2023, such as the Random Forest, Support Vector Machine, Multilayer Perceptron Neural Networks, and the Long Short-Term Memory, on various water-related aspects such as groundwater potential mapping, rainfall prediction, surface water assessment, and flood risk assessment and a host of others. There are limitations to the studies that have been reviewed, such as a lack of comprehensive historical data and the need for comparative analyses. Overall, this reviewed study emphasizes the variety of water resource modelling potentials and issues covering improving modelling accuracy and speed, as well as a thorough evaluation of the application of AI and GIS for water resource management. © 2024 Elsevier B.V.
Elsevier B.V.
23529385
English
Review

author 2-s2.0-85193004491
spellingShingle 2-s2.0-85193004491
Application of integrated artificial intelligence geographical information system in managing water resources: A review
author_facet 2-s2.0-85193004491
author_sort 2-s2.0-85193004491
title Application of integrated artificial intelligence geographical information system in managing water resources: A review
title_short Application of integrated artificial intelligence geographical information system in managing water resources: A review
title_full Application of integrated artificial intelligence geographical information system in managing water resources: A review
title_fullStr Application of integrated artificial intelligence geographical information system in managing water resources: A review
title_full_unstemmed Application of integrated artificial intelligence geographical information system in managing water resources: A review
title_sort Application of integrated artificial intelligence geographical information system in managing water resources: A review
publishDate 2024
container_title Remote Sensing Applications: Society and Environment
container_volume 35
container_issue
doi_str_mv 10.1016/j.rsase.2024.101236
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193004491&doi=10.1016%2fj.rsase.2024.101236&partnerID=40&md5=76ffb6b31b0c75a3a91ed5872b0495e7
description Climate change has exacerbated the water resources situation by producing erratic rainfall patterns, fading away ice sheets, increasing sea levels, floods, and droughts. Rising temperatures affect precipitation patterns and the entire water cycle, exacerbating water scarcity and water-related dangers including floods and droughts. As a result, modelling these water resources conundrums based on the various hydrological and meteorological variables has been challenging to ensure effective water management. The target subject of this reviewed study concerns the forecasting of water resources using artificial intelligence and/or geographical information systems, which can be useful in addressing the challenges mentioned. This study presents a few methodologies that have been proposed for modelling the processes that eventually are related to water resources availability, in 70 scientific publications published between 2019 and 2023, such as the Random Forest, Support Vector Machine, Multilayer Perceptron Neural Networks, and the Long Short-Term Memory, on various water-related aspects such as groundwater potential mapping, rainfall prediction, surface water assessment, and flood risk assessment and a host of others. There are limitations to the studies that have been reviewed, such as a lack of comprehensive historical data and the need for comparative analyses. Overall, this reviewed study emphasizes the variety of water resource modelling potentials and issues covering improving modelling accuracy and speed, as well as a thorough evaluation of the application of AI and GIS for water resource management. © 2024 Elsevier B.V.
publisher Elsevier B.V.
issn 23529385
language English
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