Search results

The purpose of this study is to propose an unbiased generalized grey relational closeness evaluation model to improve the accuracy of regional agricultural drought vulnerability decision-making results, as well as to provide theoretical support for reducing agricultural drought risk and losses.

The index weight is calculated using a rough set and deviation minimization criterion, and the relational degree between the research object and the double reference sequence is thoroughly investigated using the generalized grey relational closeness degree. Because different index rankings can correspond to different closeness degrees, the Monte Carlo method was used to calculate an unbiased estimate of the generalized grey relational closeness degree, which was used as a decision basis.

Agricultural drought vulnerability in Henan Province in 2019 was clearly spatially differentiated. The vulnerability to agricultural drought in the southern and eastern regions was generally higher than that in other regions. The evaluation results of this model are highly stable and reliable compared to those of the traditional generalized grey relational evaluation model.

This study proposes an evaluation model based on an unbiased generalized grey relational closeness degree, which is important to supplement the grey relational analysis method system and plays a positive role in promoting the quantitative evaluation of regional agricultural drought vulnerability.

The Monte Carlo method is used to calculate the unbiased estimation of the generalized grey relational closeness degree, which solves the problem of the replacement dependence of the traditional generalized grey relational degree and the one-sidedness of the evaluation results, and provides a new research idea for the evaluation of regional agricultural drought vulnerability under cross-sectional informatics.

A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.

A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.

The results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.

The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

With the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.

The linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.

By adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.

This paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.

Based on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

This paper aims to investigate drought impacts and vulnerabilities specific to mountain resort communities and the implications for the tourism industry, in order to derive a set of recommendations for reducing drought vulnerability of this economic sector.

This article presents the results from a case study conducted in Colorado, USA, mountain communities evaluating the multi‐year drought that culminated in 2002. Using qualitative research methods, a series of interviews were conducted to garner the experiences of state and local tourism officials, ski resort representatives, and environmental, municipal and agricultural organizations.

This study finds that drought alone was not responsible for creating the variety of direct and secondary impacts on Colorado resort communities. The paper highlights the importance of water resources to the economic wellbeing of resort communities and recognizes the critical roles of communication, planning, media and public perception during a drought.

Societal vulnerability in mountain resort communities in relation to drought has rarely been addressed in the literature. The study provides specific recommendations to the resort managers and tourism officials for mitigating drought impacts of, and reducing resort communities' vulnerability to, drought.

The purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.

The grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.

The drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.

It provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.

By studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.

The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very common in productive practice and scientific research, such as coal-bed methane (CBM) content analysis, civil aircraft cost analysis, etc. Key factors selection is an important basic work for SMCDA problem; the proposed method is constructed to improve the accuracy and explanatory of the selected key factors.

Using grey system theory to solve SMCDA problem is more reasonable under few data and poor information. Therefore, this paper constructs a grey incidence analysis (GIA) model with rate of change to select the key factors of an SMCDA problem. The basic idea of the proposed method is to simulate time series by randomly sorting the selected samples, and to calculate the degree of grey incidence with rate of change by loop iterative algorithm, then to construct the degree matrix of grey incidence with rate of change, and finally by which, to utilise quantitative and qualitative analysis methods to select the key factors.

The experimental analysis of application cases demonstrates that the key factors of system’s characteristic can be successfully screened out by the proposed method, the results are consistent with actual conditions, and they have a clearer meaning and a better interpretability.

The method proposed in this paper could be utilised to select key factors for such a class of SMCDA problem, which has fewer observation samples (small-sample), which is influenced by a number of factors (multi-factor) and whose observation samples are placed randomly rather than by time (cross-sectional data). Taking the key influence factors of CBM content and the key driving factors of the vulnerability of agricultural drought in Henan as examples, the results proved the feasibility and superiority of this proposed method.

Most of the existing GIA models mainly focus on these classes of issues with time series data or panel data. However, few GIA models take SMCDA problem as the research object. In this paper, the authors develop the GIA model with rate of change according to the characteristics of SMCDA problem, and present some properties and application suggestions of the proposed method.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Asia is considered one of the fastest developing continents. As per the UNDP Human Development Report (2010), Asian countries like China, Nepal, Indonesia, Lao PDR, and South Korea are among the top 10 fastest growing countries. China, the second highest achiever in the world in terms of HDI improvement since 1970, is the only country on the “Top 10 Movers” list due to income rather than health or education achievements. Significant progress in human development was also found for most of the nine South Asian countries in the trends analysis – Afghanistan, Bangladesh, India, Iran, Nepal, and Pakistan. Inequality for women remains a major barrier to human development throughout Asia, the 2010 Report shows. The new Gender Inequality Index – which captures gender gaps in reproductive health, empowerment, and workforce participation in 138 countries – shows that six countries of East Asia and the Pacific fall in the lower half on gender inequality, with Papua New Guinea among the bottom 10. Asian Development Bank (ADB)'s Asian Development Outlook (2011) forecasts regional GDP growth of 7.8% in 2011 and 7.7% the following year. The projected growth rates are lower than the 9% posted in 2010 but show that the region continues its firm recovery from the global economic crisis. It needs to be kept in mind that Asia's diversity is based on socioeconomic, demographic, climatic, and cultural characteristics.

The purpose of this paper is to propose a grey clustering model based on kernel and information field to deal with the situation in which both the observation values and the turning points of the whitenization weight function are interval grey numbers.

First, the “unreduced axiom of degree of greyness” was expanded to obtain the inference of “information field not-reducing”. Then, based on the theoretical basis of inference, the expression of whitenization weight function with interval grey number was provided. The grey clustering model and fuzzy clustering model were compared to analyse the relationship and difference between the two models. Finally, the paper model and the fuzzy clustering model were applied to the example analysis, and the interval grey number clustering model was established to analyse the influencing factors of regional drought disaster risk in Henan Province.

The example analysis results illustrate that although the two clustering methods have different theoretical basis, they are suitable for dealing with complex systems with uncertainty or grey characteristic, solving the problem of incomplete system information, which has certain feasibility and rationality. The clustering results of case study show that five influencing factors of regional drought disaster risk in Henan Province are divided into three classes, consistent with the actual situation, and they show the validity and practicability of the clustering model.

The paper proposes a new whitenization weight function with interval grey number that can transform interval grey number operations into real number operations. It not only simplifies the calculation steps, but it has a great significance for the “small data sets and poor information” grey system and has a universal applicability.