Evaluation of Water Resources Carrying Capacity in Jiangxi Province Based on Principal Component Analysis

Hu, Yangcheng; Yu, Lin; Hu, Qifan

doi:10.2112/jcr-si105-031.1

Cited by 7 publications

(6 citation statements)

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“…One is to use a comprehensive technique to assess WRCC. Approaches include principal component analysis (Hu et al 2020), fuzzy comprehensive evaluation (Abedi-Koupai et al 2022;Feng et al 2020), and fuzzy set pair analysis (Zhang et al 2020). Therefore, combined with the TOPSIS model, the combined weighted TOPSIS model (Sun et al 2020) was further developed to assess WRCC.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In terms of time selection, most studies have investigated regional WRCC in the current year (Xu et al 2022) or predicted future WRCC (Yan and Xu 2022). In terms of method selection and model construction, most research has used a comprehensive evaluation model for regional WRCC analysis (Hu et al 2020) without fully considering the limitations of indicator weight determination.…”

Section: Introductionmentioning

confidence: 99%

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Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

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The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

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“…With the deepening of research, the evaluation methods of water resources carrying capacity are improving day by day. A variety of model methods have gradually emerged, such as principal component analysis [5,6] , matter-element model [7,8] , projection pursuit [9,10] , fuzzy evaluation [11][12][13] , multi-objective decision [14][15][16] , water footprint theory [17][18][19] , system dynamics method (SD) [20][21] . With the improvement of the theoretical system, the comprehensive method coupled model evaluation system [22,23] has been further improved, such as DPSIR model [24] , PSR model [25,26] , quality-domain-flow model [27][28] and multi-dimensional cloud model [29][30][31][32] .…”

Section: Introductionmentioning

confidence: 99%

Assessment and early warning of water resources carrying capacity in Dongting Lake Ecological Economic Zone

Fu,

Ding,

Wang

et al. 2024

Preprint

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Assessment of water resource carrying capacity is a useful tool for measuring the degree of realization of resource and environmental planning. Scientific monitoring and evaluation of water resource carrying capacity is a basic requirement for building ecological civilization and implementing the development concept of "green water and green mountains are golden mountains and silver mountains". Taking 26 county units in the Dongting Lake Ecological and Economic Zone in Hunan Province as the evaluation object, 22 water resource carrying capacity evaluation indicators were selected, and the comprehensive weight method of subjective and objective combination of hierarchical entropy weight evaluation method and weighted summation comprehensive scoring method were used to calculate the comprehensive water resource carrying capacity values of 26 "county units" in the Dongting Lake Ecological and Economic Zone during the 25-year research period of 1997–2021. The results were analyzed using gray correlation method and Ridge regression modeling. After processing the input data of each layer based on the evaluation index system, BP neural network regression training was conducted to obtain a prediction model for the future trend of water resource carrying capacity for prediction and analysis. The results show that: (1) In terms of time trend, the water resource carrying capacity of the study area was generally stable, between overload and critical. The change trend of water resource carrying capacity changed greatly among districts and counties; (2) In terms of spatial distribution, there were differences in the water resource carrying capacity of the Dongting Lake ecological and economic zone: the water resource carrying capacity around the western districts and counties and the central South Dongting Lake was higher than the northern Yueyang urban area; (3) Among the four criteria layers, the weight of water resources supporting capacity was the highest, with the smallest change, and the economic supporting capacity index had the largest change. The decline in water resources carrying capacity in most districts and counties was during a period of relatively rapid economic development; (4) It was predicted that the water resource carrying capacity of Nanxian, Hanshou, and Xiangyin counties around the South Dongting Lake would significantly increase in 2025, shifting from overload to a critical state, and maintaining this trend in the coming years; (5) Dingcheng District of Changde City would gradually transform from critical to overload, and maintain this trend in the coming years. To sum up, the key to improve the water resources carrying capacity of Dongting Lake Ecological Economic Zone was to prevent water resources pollution, optimize water resources allocation and improve water resources utilization efficiency. It was recommended from our study that the government should continue to strengthen the awareness of water environment protection and water environment governance measures, and regularly conduct environmental governance performance evaluation to improve the accuracy of comprehensive governance. Based on the long-term objectives, we should formulate measures to protect Dongting Lake, promote the construction of new urbanization, and develop green industries such as ecological agriculture, ecological industry, and ecological tourism according to local conditions.

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“…When superior tourism resources are fully utilized and become tourism attractions, it will become the iconic image of the tourist destination, improve tourism awareness, and bring huge passenger flow to it. At present, the research on tourism resource attraction mainly focuses on the evaluation of tourism resource attraction from the perspectives of IPA analysis [7,8], AHP analysis [9], tourist perception perspective [10,11], network information [12], etc., constructing tourism resource attraction model [13], and exploring its spatial structure characteristics [14]. Proposed the path to enhance the attraction of tourism resources [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of attraction and spatial pattern analysis of world cultural and natural heritage tourism resources in China

Zhang

Long

2023

PLoS ONE

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The attraction of tourism resources is very important to promote the sustainable development of tourism industry. This study takes China’s world cultural and natural heritage as the research object, and constructs an attractiveness evaluation system for China’s world cultural and natural heritage tourism resources by collecting user feedback data from three major travel OTA platforms. At the same time, ArcGIS 10.7 software was used for spatial autocorrelation analysis and kernel density analysis to explore the spatial distribution pattern of tourism resource attraction. The results show that China’s world cultural and natural heritage can be subdivided into 5 main categories and 10 sub-categories. From the perspective of spatial aggregation, only the Moran’s I index of tourist resource points showing a significant spatial aggregation feature. This study is helpful to reveal the weaknesses of tourism resource points and provide reference for sustainable development of attraction and optimization of tourism planning and management.

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Evaluation of Water Resources Carrying Capacity in Jiangxi Province Based on Principal Component Analysis

Cited by 7 publications

References 0 publications

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Assessment and early warning of water resources carrying capacity in Dongting Lake Ecological Economic Zone

Evaluation of attraction and spatial pattern analysis of world cultural and natural heritage tourism resources in China

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