Spatial-Temporal Evaluation and Prediction of Water Resources Carrying Capacity in the Xiangjiang River Basin Using County Units and Entropy Weight TOPSIS-BP Neural Network

Jiacheng Wang, Zhixiang Wang, Zeding Fu, Yingchun Fang, Xuhong Zhao, Xiang Ding, Jing Huang, Zhiming Liu, Xiaohua Fu () and Junwu Liu ()
Additional contact information
Jiacheng Wang: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Zhixiang Wang: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Zeding Fu: Yueyang City Water Resources Bureau, Yueyang 414000, China
Yingchun Fang: Hunan Kaidi Engineering Technology Co., Ltd., Yueyang 414000, China
Xuhong Zhao: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Xiang Ding: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Jing Huang: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Zhiming Liu: Henan Academy of Sciences, Zhengzhou 450046, China
Xiaohua Fu: Center for Ecological Environment Management and Assessment, Central South Forestry University of Science and Technology, Changsha 410004, China
Junwu Liu: Hunan Kaidi Engineering Technology Co., Ltd., Yueyang 414000, China

Sustainability, 2024, vol. 16, issue 18, 1-27

Abstract: To improve the water resources carrying capacity of the Xiangjiang River Basin and achieve sustainable development, this article evaluates and predicts the Xiangjiang River Basin’s water resources carrying capacity level based on county-level units. This article takes 44 county-level units in the Xiangjiang River Basin as the evaluation target, selects TOPSIS and the entropy weight method to determine weights, calculates the water resources carrying capacity level of the evaluation sample, uses a BP neural network model to calculate the predicted water resources carrying capacity level for the next 5 years, and adds the GIS method for spatiotemporal analysis.(1) The water resources carrying capacity of the Xiangjiang River Basin has remained relatively stable for a long period, with overloaded areas being the majority. (2) There are relatively significant spatial differences in the carrying capacity of water resources: Zixing City, located upstream of the tributary, is far ahead due to its possession of the Dongjiang Reservoir; the water resources carrying capacity in the middle and lower reaches (northern region) is generally higher than that in the upper reaches (southern region). (3) According to the BP neural network model prediction, the water resources carrying capacity of the Xiangjiang River Basin will maintain a stable development trend in 2022, while areas such as Changsha and Zixing City will be in a critical state, and other counties and cities will be in an overloaded state.This study has important references value for the evaluation and early warning work of the Xiangjiang River Basin and related research, providing a scientific and systematic evaluation method and providing strong support for water resource management and planning in Hunan Province and other regions.

Keywords: Xiangjiang River; water resources carrying capacity; TOPSIS model; entropy weighting method; BP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/18/8184/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/18/8184/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:18:p:8184-:d:1481466

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().