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Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption o...

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Autores principales: Guo, Ting, Yu, Huiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651356/
https://www.ncbi.nlm.nih.gov/pubmed/34887912
http://dx.doi.org/10.1155/2021/2201964
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author Guo, Ting
Yu, Huiying
author_facet Guo, Ting
Yu, Huiying
author_sort Guo, Ting
collection PubMed
description Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.
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spelling pubmed-86513562021-12-08 Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data Guo, Ting Yu, Huiying Comput Intell Neurosci Research Article Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China. Hindawi 2021-11-30 /pmc/articles/PMC8651356/ /pubmed/34887912 http://dx.doi.org/10.1155/2021/2201964 Text en Copyright © 2021 Ting Guo and Huiying Yu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Guo, Ting
Yu, Huiying
Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title_full Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title_fullStr Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title_full_unstemmed Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title_short Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
title_sort evaluation of ecological water consumption in yanhe river basin based on big data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651356/
https://www.ncbi.nlm.nih.gov/pubmed/34887912
http://dx.doi.org/10.1155/2021/2201964
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