Forewarning Model of Regional Water Resources Carrying Capacity Based on Combination Weights and Entropy Principles

Zhou, Rongxing; Pan, Zhengwei; Jin, Juliang; Li, Chunhui; Ning, Shaowei

doi:10.3390/e19110574

Cited by 38 publications

(32 citation statements)

References 10 publications

(13 reference statements)

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“…RWRCC is a complex system composed of support (water resources system), pressure (economic, social, and ecological environment system), and regulation [36,37]. The EWSI can be chosen from indices for evaluation, but those with no significant change around the average value should be screened out.…”

Section: Early Warning Sign Indexmentioning

confidence: 99%

Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index

Chen

Jin

Ning

et al. 2020

IJERPH

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The sustainable utilization of water resources is a significant factor in the development of the national economy and society. Regional water resources carrying capacity (RWRCC) is an appropriate method for evaluating the balance in such utilization. In this paper, we combined time difference correlation analysis and set pair analysis firstly to identify the early warning sign index (EWSI) for RWRCC, and warning limits were determined using a logical curve. Analytic hierarchy process based on the accelerating genetic algorithm (AGA-AHP) method was used to improve the KLR model by determining weights objectively. We took advantage of the new improved model to build the aggregate warning index (AWI). Then, according to the corresponding relationship between EWSI and AWI, the early warning system for regional water resources carrying capacity (EWS-RWRCC) was established, and a case study was carried out in Anhui Province. The results showed there are eight effective EWSI obtained through the early warning analysis process of RWRCC in Anhui Province, among which the repetitive use rate of industrial water and average daily coefficient have a greater impact on AWI. Basically, the EWS-RWRCC can describe RWRCC changes in Anhui Province. From 2006 to 2014, more than half the signal lights in Anhui Province were yellow and orange, which indicated a poor state. It has been proved that the constraints of population, GDP growth and water supply capacity on the utilization of water resources in the future will be further tightened, which should be considered for future monitoring and early warning. The early warning method we used here can be widely applied into other fields; the results will enhance monitoring capacity and scientifically guide regional water resources management.

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Section: Early Warning Sign Indexmentioning

confidence: 99%

Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index

Chen

Jin

Ning

et al. 2020

IJERPH

Self Cite

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“…The information entropy theory is a comprehensive method based on probability and numerical statistics. On the basis of the information entropy theory, the entropy weight method is proposed to reflect the relative strength of each index in the interaction, and it has been widely applied to multi-factor problems, such as mountain landslide prediction [19], agriculture [19], environment [20], business decision-making [21]. These studies proved that the entropy weight method can evaluate the program scientifically.…”

Section: Of 14mentioning

confidence: 99%

Optimization and Evaluation of Ventilation Mode in Marine Data Center Based on AHP-Entropy Weight

Feng

Lei

Guo

et al. 2019

Entropy

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The ventilation mode affects the cooling efficiency of the air conditioners significantly in marine data centers. Three different ventilation modes, namely, underfloor ventilation, overhead ventilation, side ventilation, are numerically investigated for a typical marine data center. Four independent parameters, including temperature, velocity, air age, and uniformity index, are applied to evaluate the performances of the three ventilation modes. Further, the analytic hierarchy process (AHP) entropy weight model is established and further analysis is conducted to find the optimal ventilation mode of the marine data center. The results indicate that the underfloor ventilation mode has the best performance in the airflow patterns and temperature distribution evaluation projects, with the highest scores of 91.84 and 90.60. If low energy consumption is required, it is recommended to select the overhead ventilation mode with a maximum score of 93.50. The current evaluation results agree fairly well with the three dimensional simulation results, which further proves that the AHP entropy weight method is reasonable and has a high adaptability for the evaluation of air conditioning ventilation modes.

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“…For instance, when evaluating the comprehensive grade of all courses for each student in a class, the information provided by the series of the original course scores was effective, and the series should be used to calculate the corresponding entropy value and the weight of each course. Nevertheless, when The previous studies [22][23][24][25][26]28,38] mostly used the original index value to calculate the corresponding entropy value when applying the entropy weight method. However, it is not reasonable to always utilize the information provided by the original index for different study objects.…”

Section: Weight Of Each Decision-making Index In the Respective Subsymentioning

confidence: 99%

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Jiang

Jin

et al. 2019

Entropy

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To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation–projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal.

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Forewarning Model of Regional Water Resources Carrying Capacity Based on Combination Weights and Entropy Principles

Cited by 38 publications

References 10 publications

Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index

Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index

Optimization and Evaluation of Ventilation Mode in Marine Data Center Based on AHP-Entropy Weight

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

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