Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method

Liu, Zaohong; Jiang, Zhangzejun; Xu, Chen; Cai, Guanjun; Zhan, Jie

doi:10.1007/s11069-021-04744-3

Cited by 37 publications

(20 citation statements)

References 36 publications

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“…To further compare the superiority-inferiority relationships of different batches of wheat, and to verify the reasonableness and accuracy of the results of wheat quality evaluation by the model in this paper, the ranking degree of each batch of wheat was measured using Equation 13 to obtain the ranking results of each batch of wheat quality by the PCA-EWM-grey system (GS) evaluation model constructed in this paper. Meanwhile, the PCA-EWM-TOPSIS evaluation model (Liu et al, 2021) and PCA-rank sum ratio (RSR) evaluation model (Lu et al, 2022) were used as comparative models to calculate and rank the superiority and inferiority relationships of each batch of wheat, respectively. Among them, the PCA-EWM-TOPSIS evaluation model used the similarity proximity C as the scoring basis, and the PCA-RSR evaluation model used the RSR value as the scoring basis.…”

Section: Analysis Of Evaluation Resultsmentioning

confidence: 99%

A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

2023

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The objective and accurate evaluation of wheat quality is the key to ensuring the safety of wheat storage. Due to the complexity and variability of wheat quality, traditional methods, such as the evaluation of single physical and chemical index, have the problems of insufficient information and poor differentiation of fuzzy intervals. Based on grey systems theory, this paper proposes a new comprehensive wheat quality grading model. Firstly, eight physical and chemical indices are selected as the factor set of the evaluation model, and the key quality indices are analyzed and screened by principal component analysis (PCA). The entropy weight method (EWM) is then used to measure the importance of the contribution of selected indices to wheat quality information. Finally, the grey evaluation model is then used to determine the grey correlation of the evaluation levels of different batches of wheat, and to rank the superiority and inferiority. The model is applied to the comprehensive analysis and evaluation of wheat samples, verifying the feasibility and validity of the model and providing some theoretical guidance for the evaluation of wheat quality.

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Section: Analysis Of Evaluation Resultsmentioning

confidence: 99%

A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

2023

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“…The entropy weight TOPSIS method is a combination of the traditional technique for order preference by similarity to ideal solution (TOPSIS) method and the entropy method. The TOPSIS method is used to evaluate the research object according to the relative closeness between the research object and the ideal solution [ 46 ]. The entropy weight TOPSIS method can not only assess the level of geopolitical risk by comprehensively measuring the distance between the actual level of the evaluation object and the ideal level, but also overcome the shortcomings of subjective assignments which are more subjective.…”

Section: Research Methods and Data Sourcesmentioning

confidence: 99%

Geopolitical Risk Evolution and Obstacle Factors of Countries along the Belt and Road and Its Types Classification

Shan

Ruan

et al. 2023

IJERPH

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As a great practice of building a community of shared future for mankind, the Belt and Road Initiative is facing geopolitical risk brought by great power games, regional conflicts and terrorism. It is an important mission of geopolitical research to scientifically deal with the geopolitical risk along the Belt and Road. This study systematically constructs the geopolitical risk assessment index system and analyzes the spatiotemporal evolution, obstacle factors and risk types of geopolitical risk of countries along the Belt and Road by using the entropy weight TOPSIS model, obstacle degree model and minimum variance method. The research results showed that: (1) From 2005 to 2020, the polarization of geopolitical risk in countries along the Belt and Road was very significant, and the overall trend of geopolitical risk tended to deteriorate. (2) The Middle East and Eastern Europe were the most important geopolitical risk zones along the Belt and Road, and Afghanistan, Iraq, Russia and Ukraine were the main high geopolitical risk centers, with significant risk spillover effects from these centers. (3) Terrorism and close relations with the United States were the most important obstacle factors for geopolitical risk in countries along the Belt and Road, and military intervention politics, trade dependence degree and foreign debt burden were important obstacle factors for geopolitical risk in countries along the Belt and Road. (4) Geopolitical risk along the Belt and Road can be divided into sovereign risk dominant type, sovereign and military risk dominant type, sovereign and major power intervention risk dominant type, and sovereign and military and major power intervention risk jointly dominated type, among which sovereign and military and major power intervention risk jointly dominated type was the most important geopolitical risk type. In order to scientifically deal with geopolitical risk in countries along the Belt and Road, it is necessary to strengthen geopolitical risk awareness, pay attention to the dominant geopolitical risk factors, strengthen the control of regional geopolitical risk spillover and formulate reasonable risk prevention and control scheme based on geopolitical risk types.

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“…Liu et al analyzes three investors. Glouchkov verified the role played by investor sentiment and its impact on financial markets by using a combination of theoretical and empirical studies to establish the sensitivity of individual stock returns to changes in sentiment, in the form of sentiment beta, and to associate the resulting set of sentiments, which we call overall market sentiment associated with an individual stock sentiment [10].…”

Section: Related Workmentioning

confidence: 99%

Macroeconomic Multivariate Statistics and Regionalization Management Strategy Based on Random Matrix

Tan

2022

Mathematical Problems in Engineering

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This paper adopts the method of the random matrix to conduct an in-depth study and analysis of the management of macroeconomic multivariate statistics and regionalization, and to design its management strategy based on a random matrix. A combination of theoretical and applied research, combined with the experience of other cities, is used to study regional economic management using literature analysis, comparative analysis, and deductive induction by summarizing the results achieved in community management, studying its experience, exploring the significance of community management in ETDZs for social development, analyzing the current obstacles and contradictions affecting community management in Suqian ETDZ, and proposing relevant countermeasures for the problems arising therein to enrich the relevant theories and thus promote the level of community management in ETDZs to a higher level. Macroeconomic uncertainty refers to the changes that cannot be accurately observed, analyzed, and foreseen, i.e., the deviation of the expected value of the economy from the actual value. Not all macroeconomic fluctuations belong to the category of macroeconomic uncertainty, only unpredictable economic fluctuations are uncertain. It can be seen from the above that there is a corresponding relationship between the financial income correlation matrix and the random correlation matrix, and both are affected by the same random factors. The importance of uncertainty for macroeconomic policy making is undeniable, and its quantification is a key step. In this paper, we propose an econometric model to eliminate expectations and compare the advantages and disadvantages of different measurement models; we measure a system of fourteen macroeconomic indicators to reflect macroeconomic uncertainty and provide quantitative measurement results for the correct understanding of macroeconomic uncertainty and economic policy making. Factor analysis is used to analyze these six comprehensive aspects to obtain the six-dimensional comprehensive scores of each province and city, and the scores are normalized and visualized. Based on a certain understanding of the economic equilibrium structure of each province and city in China, and a certain control of the overall development of each region itself at this stage, the future economic development trend is studied.

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Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method

Cited by 37 publications

References 36 publications

A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

Geopolitical Risk Evolution and Obstacle Factors of Countries along the Belt and Road and Its Types Classification

Macroeconomic Multivariate Statistics and Regionalization Management Strategy Based on Random Matrix

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