Projection Pursuit Cluster Model Research Articles

Risk Assessment of Coal Mine Flood Disasters Based on Projection Pursuit Clustering Model

Previously conducted studies have established that as a disaster-bearing body, a coal mine is vulnerable to flood disasters and their consequent impacts. The purpose of this study is to put forward a quantitative evaluation method of the risk of coal mine flood disaster. Based on the scientific theory of disaster risk, a risk assessment model and index system for coal mine flood disaster was constructed, and a risk assessment method was proposed based on the projection pursuit and fuzzy cluster analysis. The results show that the risk of coal mine flood disaster was mainly determined by the hazard of disaster-causing factors, the stability of the disaster-prone environment, and the vulnerability of disaster-bearing bodies. Further research shows that the maximum daily rainfall had the greatest impact the risk of coal mine flood disaster. Therefore, the early warning mechanism should be established between the coal mine and the meteorological department to improve the fortification level. A risk assessment method of coal mine flood disaster was proposed in this study, which is of great significance for energy sustainability.

New Method for Sugarcane (Saccharum spp.) Variety Resources Evaluation by Projection Pursuit Clustering Model

In the breeding of new sugarcane varieties, the survey data do not always conform with a normal or linear distribution. To apply non-normal or non-linear data to evaluate new material requires a suitable evaluation model or method. The projection pursuit clustering (PPC) model is a statistical method that does not require making normal assumptions or other model assumptions on sample data, and is suitable to analyze high-dimensional, non-linear, and non-normal data. However, this model has been applied infrequently to crop variety evaluation. In this study, 103 varieties that have been bred over the last 70 years in China were planted, and their main industrial and agronomic traits were collected. Through the exploratory analysis of the data structure characteristics, the PPC model was used to evaluate these sugarcane varieties. The model provided good projection directions of agronomic and industrial traits, with accurate projection values. PPC models could evaluate sugarcane resources well, and the results were objective and reliable. Thus, the PPC model could be used as a new method for crop variety evaluation. At the same time, 51 excellent industrial and agronomic variety resources were screened for application in breeding.

Research on comprehensive benefits and reasonable selection of marine resources development types

AbstractThe comprehensive benefit evaluation of marine resources developmental model is of great significance to choose the appropriate types of marine resources development and promote the intensive utilization and sustainable development of marine resources. This article examined five types of marine resources development, such as marine protected areas, mariculture, offshore wind power, sewage dumping, and land reclamation, and constructed a three-level evaluation index system for the comprehensive benefits of marine resources development. The projection pursuit clustering model was used to evaluate and analyze the comprehensive benefits and main influencing factors of 15 marine resources development projects in Jiangsu Province, China. It was found that the comprehensive benefit projection values of marine protected areas and offshore wind power are higher. The projection value of comprehensive benefit of land reclamation is the lowest. The main influencing factors include but not limited to the change rate of total output of aquatic products, contradiction between management and marine use, negative impact on residents’ lives, etc. The research results have important guiding significance for promoting the rational development and utilization of marine resources and the high-quality development of the oceans.

Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model

The vulnerability assessment indicator system (VAIS), including the tourism economic sensitivity and respondence, is modified and established in this paper. According to the collected data, during 2014–2018 of the 31 provinces of China, of the tourism economy sensitivity and respondence, the improved comprehensive evaluation projection pursuit clustering (PPC) model is established, and the vulnerability indexes of the 31 provinces are calculated, thus expanding the tourism economic vulnerability assessment methods. Our empirical results show that, during the period of 2014 to 2018, the sensitivity, the respondence, and the vulnerability indexes are unbalanced overall. The tourism economy sensitivity and the respondence show that the spatiotemporal distribution characteristics are high in the east and low in the west. On the contrary, as for the vulnerability, the spatiotemporal distribution characteristics are low in the east and high in the west. Among the 40 indicators, the ratio of industrial solid waste utilized (%), urbanization rate, and the density of grade highway and railway network (km/km2) have the greatest impact on the respondence, while the proportion of the population affected by natural disasters, the diversification index of industrial structure, and the number of traffic accident casualties have the most significant impact on the sensitivity, which are the indicators that have the greatest impact on vulnerability. Therefore, in order to effectively reduce sensitivity, improve respondence, and thus reduce the vulnerability index of the tourism economy, the provinces should first improve the above-mentioned evaluation indicators with the largest weights. Our research results in this paper enrich the theory of sustainable development of the tourism industry and derive managerial and policy insights for further achieving the high-quality development of the tourism economy.

Temporal and spatial variation characteristics of disaster resilience in Southwest China’s mountainous regions against the background of urbanization

With the growth in urbanization and the increasing challenges associated with climate change, disaster resilience has become a hot spot for researchers worldwide and significant progress in this field has been achieved. Disaster resilience is an important criterion in the judgment of a region’s ability to absorb, adapt to, and recover from a disaster. In this study, an evaluation system that is capable of reflecting large-scale disaster resilience for Southwest China’s mountainous regions was constructed. Disaster resilience indexes of a typical province in the mountainous areas of Southwest China—Sichuan Province and Yunnan Province—between 2000 and 2015 were calculated using the projection pursuit clustering (PPC) model. The temporal and spatial variation characteristics of disaster resilience were analyzed by combining them with the spatial autocorrelation principle. Additionally, the temporal and spatial coupling relations between disaster resilience and urbanization level were comprehensively analyzed. Results demonstrated that disaster resilience increased continuously across the study area during the period between 2000 and 2015. Differences in disaster resilience across different counties were observed to first increase and then decrease continuously. However, the regional disparity disaster resilience was still obvious. The spatial distribution of disaster resilience was generally high in the Southeast and low in the Northwest along the Hu Huanyong line. Disaster resilience across the region presented a significant spatial autocorrelation in different years and the agglomeration characteristics generally improved. Furthermore, a significant spatial variation was observed in the disaster resilience change rate. Results also showed a linear relationship between disaster resilience and urbanization level. Moreover, there is a positive spatial correlation between disaster resilience and urbanization level, demonstrating significant spatial agglomeration features. The consistent promotion of high-quality urbanization is extremely important in terms of improvements to a region’s capacity to cope with disasters and to achieve a management level that is comprehensive and sustainable.

Optimisation of cascade reservoir operation considering environmental flows for different environmental management classes

Abstract Most commonly, hydropower operation focused on the economic and social benefits of the humankind, whereas neglecting the riverine ecosystem conservation. This conventional approach of operation led to the degradation of the riverine ecosystem. As research progressed, adoption of natural flow variability in the operation of reservoir gained momentum. Flow variability is a major driver of the healthy river ecosystem, whereby discharging a certain amount of water could potentially reduce the adverse effect of reservoirs and dams on the riverine ecosystem. On this preface, the study presented here constitutes a multi-objective optimisation model of cascade reservoirs to maximise the power generation while minimising the shortage of environmental flows considering five environmental management classes. This paper analyses the case studies for Liujiaping-Yumitan cascade reservoirs. The environmental flows regarding each environmental management classes were calculated using “Global Environmental Flow Calculator”, the model was solved using an anon-dominated sorting genetic algorithm (NSGA-II). The NSGA-II helps to find Pareto optimal sets of solutions, NSGA-II, however, cannot be used solely for taking a comprehensive decision. Therefore, the Projection Pursuit Cluster (PPC) model has been used additionally for the optimal sequencing of the compromised optimal solutions. The maximum annual power production considering five environmental management classes were respectively: 1.121, 1.109, 1.103, 1.109, and 1.117 (x108 kWh) with environmental flows shortage of 2060.82, 616.497, 200.52, 28.88, and 0.004 (x104 m3) respectively. Findings resulted from this study, highlight the necessity for power production compromise for a better degree of environmental protection. In contrast, the environmental benefit is more important not only for the sustainable operation of the hydropower but also for fulfilling the universal needs of the riverine ecosystem. Therefore, the outcomes of this study can be a guideline for decision-makers to improve the comprehensive benefits of the Liujiaping-Yumitan cascade hydropower station along with the conservation of the environmental health of the river.

Analysis and Comprehensive Evaluation of Water Use Efficiency in China

Proper water use requires its monitoring and evaluation. An indexes system of overall water use efficiency is constructed here that covers water consumption per 10,000 yuan GDP, the coefficient of effective utilization of irrigation water, the water consumption per 10,000 yuan of industrial value added, domestic water consumption per capita of residents, and the proportion of water function zone in key rivers and lakes complying with water-quality standards and is applied to 31 provinces in China. Efficiency is first evaluated by a projection pursuit cluster model. Multidimensional efficiency data are transformed into a low-dimensional subspace, and the accelerating genetic algorithm then optimizes the projection direction, which determines the overall efficiency index. The index reveals great variety in regional water use, with Tianjin, Beijing, Hebei, and Shandong showing highest efficiency. Shanxi, Liaoning, Shanghai, Zhejiang, Henan, Shanxi, and Gansu also use water with high efficiency. Medium efficiency occurs in Inner Mongolia, Jilin, Heilongjiang, Jiangsu, Hainan, Qinghai, Ningxia, and Low efficiency is found for Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, and Xinjiang. Tibet is the least efficient. The optimal projection direction is a* = (0.3533, 0.7014, 0.4538, 0.3315, 0.1217), and the degree of influence of agricultural irrigation efficiency, water consumption per industrial profit, water used per gross domestic product (GDP), domestic water consumption per capita of residents, and environmental water quality on the result has decreased in turn. This may aid decision making to improve overall water use efficiency across China.

Examining Social Vulnerability and Inequality: A Joint Analysis through a Connectivity Lens in the Urban Agglomerations of China

China is vulnerable to climate change. Developing the ability to assess social vulnerability and inequality amid climate change will be imperative to ensure that adjustment policies can be developed for various groups and build resilient livelihoods in China. This paper examines social vulnerability and inequality through a joint analysis of urban agglomerations. Based on a conceptual framework of social vulnerability from a network perspective, the social vulnerability index of individual cities is quantified with a projection pursuit cluster model, the social vulnerability index of cities in urban networks is calculated with the Baidu Index, and an inequality analysis is measured by the Theil index. We pilot this study in three urban agglomerations: the Jing-Jin-Ji region, the Yangtze River Delta region, and the Pearl River Delta. Our results show the following: (1) The indicator of “GDP” with the weight value reaching 0.42 has the most influence on social vulnerability. Three indicators, which are fully described herein—“Children”, “Illiterate”, and “Higher education graduated”—contribute much to social vulnerability index with values between 0.3 and 0.4. These three indicators should receive more attention in integrated risk management. (2) In the Jing-Jin-Ji region, the Theil indexes of two indicators, “Ethnic minorities” and “Green”, exceed 0.65 and have the most influence on inequality. In the Yangtze River Delta, three indicators of “Poor”, “GDP”, and “Green” contribute much to inequality. In the Pearl River Delta, the inequalities of “Green”, “Houses with no tap water” and “Higher education graduated” are high. These indicators give advance warning of potential problems, so adjustment is recommended for reducing inequality. (3) Though the connectivity structure of the Yangtze River Delta is more complicated and stronger than that of the other two agglomerations, its inequality of connectivity is higher than the others. (4) Connectivity is key for reducing social vulnerability, on the one hand, but can result in more inequality of social vulnerability, on the other hand. Therefore, it’s crucial for government to attach more significance and provide more support to cities with a higher social vulnerability index.

A New Framework for Understanding Urban Social Vulnerability from a Network Perspective

Rapid urbanization in China has strengthened the connection and cooperation among cities and has also led urban residents to be more vulnerable in adverse environmental conditions. Vulnerability research has been an important foundation in urban risk management. To make cities safe and resilient, it is also necessary to integrate the connection among cities into a vulnerability assessment. Therefore, this paper proposed a new conceptual framework for urban social vulnerability assessment based on network theory, where a new dimension of social vulnerability (connectivity) was added into the framework. Using attribute data, the traditional social vulnerability index of a city (SVInode) was calculated via the projection pursuit cluster (PPC) model. With the relational data retrieved from the Baidu search index, a new dimension (connectivity) of social vulnerability (SVIconnectivity) was evaluated. Finally, an integrated social vulnerability index (SVIurban) was measured combined with SVInode and SVIconnectivity. This method was applied in the Yangtze River Delta region of China, where the top three high values of SVInode belonged to the cities of Taizhou (Z), Jiaxing, and Huzhou. The three lowest cities were Hangzhou, Nanjing, and Shanghai. For SVIurban, the social vulnerability of cities in different hierarchies behaved differently. For Hierarchies 2 and 3, when compared to SVInode, the SVIurban was significantly reduced. However, the variation between SVInode and SVIurban in Hierarchy 4 was slight. Furthermore, an increase for the city of Taizhou (J) in its social vulnerability was achieved after connecting to the network. Huzhou, in Hierarchy 5, increased its social vulnerability the most when adding connectivity in the social vulnerability assessment. Based on the results of our case study, a conclusion was drawn that network connectivity had an influence on social vulnerability. However, when connectivity was strong enough, it could help cities to mitigate their traditional social vulnerability, whereas a loose connection in the network aggregated their traditional social vulnerability. Hence, the latter should be emphasized in future urban risk management.

An economic-efficiency analysis of water conservancy investment in Heilongjiang Province based on projection pursuit clustering and DEA models

Abstract Water conservancy investment affects economic development. To scientifically analyze its economic efficiency, this paper employs a projection pursuit clustering model based on the real-coded accelerated genetic algorithm to reduce the number of output index systems for each year, and the relative economic efficiency is obtained for 13 cities in Heilongjiang Province during the period 2007–2012 using a data envelopment analysis (DEA) model. The spatial distribution regulation is described based on the average efficiency derived for the period 2007–2012. Finally, the output values are optimized for ineffective cities under the optimization principle according to the 2012 values. The results show that the output index system is different for each year. Most cities exhibit comprehensive effectiveness, and the development of water conservancy is generally good. The spatial distribution regulation of economic efficiency tends to be high in the south and low in the north because it is related to local economic development and water policies. To achieve comprehensive effectiveness, the output indices need to be adjusted. This study assists with the promotion of efficient development plans for water conservancy systems and the economic efficiency of water conservancy investment in Heilongjiang Province.

Mapping Social Vulnerability to Air Pollution: A Case Study of the Yangtze River Delta Region, China

Many frequent and severe air pollution incidents have emerged across the vast parts of China recently. The identification of factors and mapping social vulnerability has become extremely necessary for environmental management and sustainable development. However, studies associating social vulnerability with air pollution remain sparse. With reference to research achievements of social vulnerability, this study made a new trial regarding social vulnerability assessment to air pollution. With the projection pursuit cluster (PPC) model, the top three factors contributing to social vulnerability index (SVI) were discovered and SVI and SVI dimensions (susceptibility, exposure, and adaptability) were evaluated. Results revealed that adaptability values are higher than susceptibility and exposure values. SVI is in a poor condition as, for the whole region, most values belong to the high-medium level. High SVI values mainly appear in the northern and the southern ends of study area. SVI in Shanghai is lower than in Jiangsu and Zhejiang provinces. On the scale of prefecture-level city, it can be found that the low-value centers of SVI always occurred in urban core areas. The spatial variation and inequality in social vulnerability provide policy-makers a scientific basis for air pollution prevention and sustainable management.

Evaluation of China’s Financial Risk Using Projection Pursuit Clustering Model and Its Positive Research

Evaluation of China’s Financial Risk Using Projection Pursuit Clustering Model and Its Positive Research

Fluorescence excitation–emission matrix spectra coupled with parallel factor and regional integration analysis to characterize organic matter humification

The present several humification indexes cannot provide the whole fluorescence information on organic matter composition and the evaluation results from them are inconsistent sometimes. In this study, fluorescence excitation–emission matrix spectra coupled with parallel factor analysis and fluorescence regional integration analysis were utilized to investigate organic matter humification, and the projection pursuit cluster (PPC) model was applied to form a suitable index for overcoming the difficulties in multi-index evaluation. The result showed that the ratio between the volume of humic- and fulvic-like fluorescence region and the volume of protein-like fluorescence region not only revealed the heterogeneity of organic matter, but also provided more accurate information on organic matter humification. In addition, the results showed that the PPC model could be used to characterize integrally the humification, and the projected characteristic value calculated from the PPC model could be used as the integrated humification evaluation index.

Assessment of social vulnerability to natural hazards in the Yangtze River Delta, China

China is exposed to a wide range of natural hazards, and disaster losses have escalated over the past decade. Owing to the pressure from natural disasters, along with changes in climate, social conditions, and regional environment, assessment of social vulnerability (SV) to natural hazards has become increasingly urgent for risk management and sustainable development in China. This paper presents a new method for quantifying SV based on the projection pursuit cluster (PPC) model. A reference social vulnerability index (SVI) at the county level was created for the Yangtze River Delta area in China for 1995, 2000, 2005, and 2009. The result of social vulnerability assessment was validated using data of actual losses from natural disasters. The primary findings are as follows: (i) In the study area, the major factors that impact SVI are regional per capita GDP and per capita income. (ii) The study area was more vulnerable in 1995 than in later years. SV of the whole region had decreased over the study period. (iii) Most part of Shanghai and the southeast part of Jiangsu Province had been the least vulnerable within the region. From this least vulnerable zone to the periphery of the region, the situation deteriorated. The highest SVI values in all evaluated years were found in the northern, western, or southern tips of the Yangtze River Delta.

Water Quality Analysis on Haizhou Bay Water Area of China Using the PPCM-PCAM Method

The offshore region of Haizhou Bay is characterized by intense anthropogenic activities. And the study on water quality status analysis in this offshore region has attracted more and more attention of the researchers and decision-makers. In this paper, an improved method (the PPCM-PCAM) which combines projection pursuit cluster model (PPCM) with principal component analysis method (PCAM) was developed to analysis water quality status. By using this improved method, water quality analysis of Haizhou Bay water area from January 2007 to December 2007 was studied. The analysis results show that the samples in Haizhou Bay water area can be described into 4 "category 1”, 4 “category 2” and 2 “category 3” states in 2007.

Regional partitioning of agricultural non-point source pollution in China using a projection pursuit cluster model

A projection pursuit cluster (PPC) model was used to analyze the regional partitioning of agricultural non-point source pollution in China. The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function. A novel optimization algorithm called Free search (FS) was introduced to optimize the projection direction of the PPC model. By making the appropriate improvements as we explored the use of the algorithm, it became simpler, and developed better exploration abilities. Thus, the multi-factor problem was converted into a single-factor cluster, according to the projection, which suc- cessfully avoided subjective disturbance and produced objective results. The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China, indicating that the PPC model is a powerful tool in multi-factor cluster analysis, and could be a new method for the regional partitioning of agricultural non-point source pollution.

Application of Projection Pursuit Clustering Method in the Evaluation of Coal Mine Safety

An effective and general model — projection pursuit clustering model is proposed in this paper. It can solve the incompatible problems of indexes of safety evaluation，which are uncertain，fuzzy and complex. At the same time, it improves the recognition rate of the coal mine safety evaluation model. It uses genetic algorithms to find optimal solutions for the model, the information of which can be used to research the influence of all the indexes on the evaluation of coal mine safety. The paper clusters the safety level of each coal mine according to each projection value, which provides the objective basis for decision-making of the evaluation of coal mine safety. This paper combining with the example demonstrates operation process. It also proves the method is scientific and feasible, which is of significance of coal mine safety evaluation.

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully; 2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

Projection pursuit cluster model based on genetic algorithm and its application in Karstic water pollution evaluation

A projection pursuit cluster model is used to resolve the problem of Karstic water pollution evaluation. First, the projection pursuit cluster model is stated. Then, a genetic algorithm is applied to optimise the projection direction of the projection pursuit cluster model, and the steps of the genetic algorithm are introduced in detail. Finally, a case study of Karstic water pollution evaluation is presented. The projection pursuit cluster model is a powerful tool in analysing complex multifactor problems, and it is also a new method for Karstic water pollution evaluation.