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Improved Combination Weighted Prediction Model of Aquifer Water Abundance Based on a Cloud Model
[Image: see text] The sandstone aquifer is an important underground water storage space, and the study of its water abundance is of great significance to ensure the safety of underground engineering and to explore the occurrence mechanism of groundwater sources. Based on the correlation between geol...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558245/ https://www.ncbi.nlm.nih.gov/pubmed/36249369 http://dx.doi.org/10.1021/acsomega.2c04162 |
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author | Cheng, Wenju Dong, Fangying Tang, Ruqian Yin, Huiyong Shi, Longqing Zhai, Yutao Li, Xin |
author_facet | Cheng, Wenju Dong, Fangying Tang, Ruqian Yin, Huiyong Shi, Longqing Zhai, Yutao Li, Xin |
author_sort | Cheng, Wenju |
collection | PubMed |
description | [Image: see text] The sandstone aquifer is an important underground water storage space, and the study of its water abundance is of great significance to ensure the safety of underground engineering and to explore the occurrence mechanism of groundwater sources. Based on the correlation between geological characteristics and aquifer water abundance, this paper proposed an aquifer water abundance prediction model based on a cloud model that improved combination weighting. The model took the roof sandstone aquifer of the Qingshuiying Coalfield as an example and selected five basic geological indicators that are closely related to the water-rich influence degree of the aquifer as evaluation indicators. The model was based on the idea of game theory, combined the analytic hierarchy process (AHP) and the entropy weight method, and introduced the cloud model evaluation method. The establishment of the model was based on the idea of game theory, combining the AHP and the entropy weight method and introducing the cloud model evaluation method. The results show that most of the study areas are located in weak or relatively weak water abundance areas; relatively strong water abundance areas are mainly distributed in the central, western, and southeastern parts of the study; strong water abundance areas are scattered in parts of the northeast, southwest, and southeast. The unit water inflow data of the actual pumping test is consistent with the water-rich prediction partition, which proves the accuracy and scientificity of the method. The model provides a new idea for the study of groundwater geology and a new method for predicting the water abundance of the roof aquifer in coal mines. |
format | Online Article Text |
id | pubmed-9558245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-95582452022-10-14 Improved Combination Weighted Prediction Model of Aquifer Water Abundance Based on a Cloud Model Cheng, Wenju Dong, Fangying Tang, Ruqian Yin, Huiyong Shi, Longqing Zhai, Yutao Li, Xin ACS Omega [Image: see text] The sandstone aquifer is an important underground water storage space, and the study of its water abundance is of great significance to ensure the safety of underground engineering and to explore the occurrence mechanism of groundwater sources. Based on the correlation between geological characteristics and aquifer water abundance, this paper proposed an aquifer water abundance prediction model based on a cloud model that improved combination weighting. The model took the roof sandstone aquifer of the Qingshuiying Coalfield as an example and selected five basic geological indicators that are closely related to the water-rich influence degree of the aquifer as evaluation indicators. The model was based on the idea of game theory, combined the analytic hierarchy process (AHP) and the entropy weight method, and introduced the cloud model evaluation method. The establishment of the model was based on the idea of game theory, combining the AHP and the entropy weight method and introducing the cloud model evaluation method. The results show that most of the study areas are located in weak or relatively weak water abundance areas; relatively strong water abundance areas are mainly distributed in the central, western, and southeastern parts of the study; strong water abundance areas are scattered in parts of the northeast, southwest, and southeast. The unit water inflow data of the actual pumping test is consistent with the water-rich prediction partition, which proves the accuracy and scientificity of the method. The model provides a new idea for the study of groundwater geology and a new method for predicting the water abundance of the roof aquifer in coal mines. American Chemical Society 2022-09-29 /pmc/articles/PMC9558245/ /pubmed/36249369 http://dx.doi.org/10.1021/acsomega.2c04162 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Cheng, Wenju Dong, Fangying Tang, Ruqian Yin, Huiyong Shi, Longqing Zhai, Yutao Li, Xin Improved Combination Weighted Prediction Model of Aquifer Water Abundance Based on a Cloud Model |
title | Improved Combination
Weighted Prediction Model of
Aquifer Water Abundance Based on a Cloud Model |
title_full | Improved Combination
Weighted Prediction Model of
Aquifer Water Abundance Based on a Cloud Model |
title_fullStr | Improved Combination
Weighted Prediction Model of
Aquifer Water Abundance Based on a Cloud Model |
title_full_unstemmed | Improved Combination
Weighted Prediction Model of
Aquifer Water Abundance Based on a Cloud Model |
title_short | Improved Combination
Weighted Prediction Model of
Aquifer Water Abundance Based on a Cloud Model |
title_sort | improved combination
weighted prediction model of
aquifer water abundance based on a cloud model |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558245/ https://www.ncbi.nlm.nih.gov/pubmed/36249369 http://dx.doi.org/10.1021/acsomega.2c04162 |
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