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Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area

The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resou...

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Detalles Bibliográficos
Autores principales: Li, Yang, Xie, Jiancang, Jiang, Rengui, Yan, Dongfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321107/
https://www.ncbi.nlm.nih.gov/pubmed/34324531
http://dx.doi.org/10.1371/journal.pone.0254547
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author Li, Yang
Xie, Jiancang
Jiang, Rengui
Yan, Dongfei
author_facet Li, Yang
Xie, Jiancang
Jiang, Rengui
Yan, Dongfei
author_sort Li, Yang
collection PubMed
description The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resources in the irrigation area is constructed. With the goal of maximizing comprehensive benefits, the optimal allocation of water quality and quantity of water resources is determined. Finally, the actual effect of the model is verified through specific instance data in a province. Results demonstrate that the proposed irrigation area ecological prediction model based on edge computing and GIS images can provide better performance than other state of the art models on water resources prediction. Specifically, the accuracy can remain above 90%. The proposed model for ecological water demand prediction in the irrigation area and optimal allocation of water resources is based on the principle of quality water supply. The optimal allocation of water resources reveals the sustainable development ideas and the requirements of the optimal allocation model, which is very reasonable. The improvement of the system is effective and feasible, and the optimal allocation results are reasonable. This allocation model aims at the water quality and quantity conditions, water conservancy project conditions, and specific water demand requirements in the study area. The calculation results have great practicability and a strong guiding significance for the sustainable utilization and management of the irrigation area.
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spelling pubmed-83211072021-07-31 Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area Li, Yang Xie, Jiancang Jiang, Rengui Yan, Dongfei PLoS One Research Article The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resources in the irrigation area is constructed. With the goal of maximizing comprehensive benefits, the optimal allocation of water quality and quantity of water resources is determined. Finally, the actual effect of the model is verified through specific instance data in a province. Results demonstrate that the proposed irrigation area ecological prediction model based on edge computing and GIS images can provide better performance than other state of the art models on water resources prediction. Specifically, the accuracy can remain above 90%. The proposed model for ecological water demand prediction in the irrigation area and optimal allocation of water resources is based on the principle of quality water supply. The optimal allocation of water resources reveals the sustainable development ideas and the requirements of the optimal allocation model, which is very reasonable. The improvement of the system is effective and feasible, and the optimal allocation results are reasonable. This allocation model aims at the water quality and quantity conditions, water conservancy project conditions, and specific water demand requirements in the study area. The calculation results have great practicability and a strong guiding significance for the sustainable utilization and management of the irrigation area. Public Library of Science 2021-07-29 /pmc/articles/PMC8321107/ /pubmed/34324531 http://dx.doi.org/10.1371/journal.pone.0254547 Text en © 2021 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Yang
Xie, Jiancang
Jiang, Rengui
Yan, Dongfei
Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title_full Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title_fullStr Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title_full_unstemmed Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title_short Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area
title_sort application of edge computing and gis in ecological water requirement prediction and optimal allocation of water resources in irrigation area
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321107/
https://www.ncbi.nlm.nih.gov/pubmed/34324531
http://dx.doi.org/10.1371/journal.pone.0254547
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