Research on Investment Risk Assessment of Eco-Materials Industry

2013 ◽  
Vol 347-350 ◽  
pp. 1018-1021
Author(s):  
Hong Xia Jin ◽  
He Ping Yao ◽  
Jie Yu
Keyword(s):  
Risk Assessment ◽  
Rough Set ◽  
Research Problem ◽  
Assessment System ◽  
Ecological Environment ◽  
Support Vector ◽  
Investment Risk ◽  
Classification Rules ◽  
Positive Research ◽  
Qualitative Variables

With the rapidly deteriorating of ecological environment and depletion of resources, construction investment of eco-materials industry is gradually increasing , so the investment risk assessment has become a hot research problem at present. In this paper, a new investment risk assessment system for eco-materials industry is presented, which combines rough set approach and support vector machine (SVM). It is different from traditional statistical methods. We can get reduced information table by rough set, which implies that the number of index and qualitative variables is reduced with no information loss by rough set approach. And then, this reduced information is used to develop classification rules, and SVM is trained to infer appropriate parameters. The result of the positive research indicated that this system is very valid for investment risk assessment of eco-materials industry and it will have a good application prospect in this area.

scholarly journals Eco-environmental assessment model of the mining area in Gongyi, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ying Wang ◽  
Xueling Wu ◽  
Siyuan He ◽  
Ruiqing Niu
Keyword(s):  
Environmental Assessment ◽  
Evaluation System ◽  
Mining Area ◽  
Assessment System ◽  
Assessment Model ◽  
Classification And Regression Tree ◽  
Ecological Environment ◽  
Support Vector ◽  
Entire Study ◽  
The Impact

AbstractThe ecological environment directly affects human life. One of the ecological environmental issues that China is presently facing is deterioration of the ecological environment due to mining. The pollution produced by mining causes the destruction of land, water bodies, the atmosphere, and vegetation resources and new geological problems that seriously impact human civilization and life. The main purpose of this study is to present an environmental assessment model of mine pollution to evaluate the eco-environment of mining. This study added mineral species and mining types into the factor layers and built an improved evaluation system to accurately evaluate the impact of mines on the eco-environment. In the non-mining area, the grades of the eco-environment were divided according to the Technical Criterion for Ecosystem Status Evaluation standard document. In the mining area, the grades of the assessment for the eco-environment were classified by a field survey. After comparing the accuracy of various methods, the support vector machine (SVM) model, with an accuracy of 94.8%, was chosen for the mining area, and the classification and regression tree (CART) model, with an accuracy of 89.36%, was chosen for the non-mining area. Finally, environmental assessment maps for the entire study area were generated. The results indicate that the mine environmental assessment system established by this study avoids the subjective limitations of traditional assessment methods, provides an effective method for assessing ecological quality, and will help relevant departments to plan for mine resources.

Investment Risk Assessment of Hydraulic Engineering Based on Rough Set Theory

2014 ◽  
Vol 584-586 ◽  
pp. 2640-2643
Author(s):  
Zhi Ding Chen ◽  
Hai Man Gao ◽  
Qi Guo
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Hydraulic Engineering ◽  
New Method ◽  
Decision Table ◽  
Investment Risk

The rough set theory is a new method for analyzing and dealing with data. By using this theory, we proposed a risk assessment algorithm based on rough set theory, which was described in detail in this paper. the decision table can be simplified and redundant attributes can be got rid of A method of inference based on the knowledge of rough sets and an example to show how to acquire the rules of new decision making, thus filling the method with a practical and publicizing value are given.

The Total Life-Cycle Risk Assessment of a Harbor Highway BT Project

2013 ◽  
Vol 671-674 ◽  
pp. 3059-3064
Author(s):  
Chun Yuan Liu ◽  
Ming Zhe Li
Keyword(s):  
Risk Assessment ◽  
Life Cycle ◽  
Rough Set ◽  
Rough Set Theory ◽  
Assessment System ◽  
Construction Process ◽  
Cycle Model ◽  
Actual Risk ◽  
Method Of Assessment ◽  
Total Life

This article focuses on the method of assessment of the risks in the construction process of Highway BT projects. This paper makes the risk assessment to a harbor Highway BT project, by building the risk assessment system based on the total life-cycle model. The risk assessment to the project is divided into four phases, which are initiated, contract, construction, and repurchase. And through the surveys of experts, we collect the data, determine the weights with Rough Set theory, and analyze the risks of the project. Finally, we get the result on the assessment for the risks of the project, it is a good indication of the actual risk of the project.

An improved density-based approach to risk assessment on railway investment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jingwei Guo ◽  
Ji Zhang ◽  
Yongxiang Zhang ◽  
Peijuan Xu ◽  
Lutian Li ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Rough Set ◽  
Calculation Method ◽  
Spatial Clustering ◽  
Classification Model ◽  
Risk Indicators ◽  
Investment Risk ◽  
Parameter Setting ◽  
Content Type ◽  
Dbscan Algorithm

PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

scholarly journals RISK ASSESSMENT IN TOURISM SYSTEM USING A FUZZY SET AND DOMINANCE-BASED ROUGH SET

2016 ◽  
Vol 22 (4) ◽  
pp. 554-573 ◽  
Author(s):  
Hassanali FARAJI SABOKBAR ◽  
Athareh AYASHI ◽  
Ali HOSSEINI ◽  
Audrius BANAITIS ◽  
Nerija BANAITIENĖ ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Rough Set ◽  
Fuzzy Inference ◽  
Two Dimensions ◽  
Assessment System ◽  
Rule Base ◽  
Base System ◽  
Tourism Destination ◽  
Inference System ◽  
The Impact

The purpose of this study is to identify risks, discover rule base structure and the impact of risks by knowledge base system design in one of the Iran tourism destination. Based on tourism system approach, the factors of risks are divided in two dimensions: internal risks and external risks and seven criteria: political, economic, cultural-social, technological, environmental-health, functional and safe-security. Data were analyzed by fuzzy inference system and Dominance-based Rough Set Approach (DRSA) synthesizing to construction of forecasting risk assessment system. Tourists’ perspectives towards the possibilities of risks were first assessed within seven risk factors and converted into a systematic structure within the structure of rough sets. Designing of a fuzzy expert system was dealt with using the created knowledge database. Then, the system’s sensitivity analysis was examined. The results indicate that the system can be a good way to estimate the risks and their fluctuation rates and impacts on the development of tourism destinations. The technological, social, functional and safety-security risks had the highest values in the system designed for minimum travel repeatability. The research suggests that it is important impact of risks and their interaction with each other on the future development of tourism destination.

scholarly journals Risk Assessment of Biological Asset Mortgage Loans of China’s New Agricultural Business Entities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shuzhen Zhu ◽  
Yutao Chen ◽  
Wenwen Wang
Keyword(s):  
Risk Assessment ◽  
Large Scale ◽  
Assessment System ◽  
Mortgage Loan ◽  
Support Vector ◽  
Importance Value ◽  
Mortgage Loans ◽  
Loan Risk ◽  
Agricultural Business ◽  
Business Entities

The large-scale proliferation of China’s new type of agricultural entities has given rise to a higher demand for funds. Farmers have insufficient effective collateral, which makes it difficult for them to obtain sufficient loans. Chinese financial institutions have developed a biological asset mortgage loan business to cope with this situation. China has not considered biological mortgages but has been using real estate and asset mortgage models with strong realizability. This innovative financial business has achieved positive results since it was attempted, but it also faces many risks. It is very important to comprehensively and accurately consider the risk factors of biological asset mortgage loans. Based on 1249 production and operation data samples of new agricultural entities in Zhejiang, Henan, and Shandong provinces, this study constructs an XGBoost model for empirical analysis and compares it with logical regression, support vector machine, and random forest algorithms to obtain the optimal model and feature importance value. According to the characteristic importance value, a biological asset mortgage loan risk assessment system with 4 primary indicators and 20 secondary indicators is established, which can effectively identify the biological asset mortgage loan risk of new agricultural entities.

scholarly journals Dynamic Risk Assessment of the Overseas Oil and Gas Investment Environment in the Big Data Era

2021 ◽  
Vol 9 ◽  
Author(s):  
Xuqiang Duan ◽  
Xu Zhao ◽  
Jianye Liu ◽  
Shuquan Zhang ◽  
Dongkun Luo
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Oil And Gas ◽  
Investment Decision ◽  
Assessment System ◽  
Investment Risk ◽  
Environment Change ◽  
Investment Environment ◽  
Dynamic Risk Assessment ◽  
Service Projects

Our research aims to analyze how the uncertainties and risks of the overseas oil & gas investment environment change over time and reveal the specific occurrence probabilities of risk on different levels. In the process of long-drawn overseas oil & gas investment that can last for 30 years or longer, it is difficult for investment decision-makers to grasp the occurrence probabilities and trends of some specific risks accurately and in a timely manner. The overseas risk assessment system has made great progress; however, it has remained elusive due to the challenge of too many complex and interweaved factors. With the advent of big data and artificial intelligence, more precise and specific risk evaluations can be conducted. Our research selects 25 indicators from six dimensions and applies a Cloud parameter Bayesian network algorithm to dynamically assess the oil and gas overseas investment risk of 10 countries. The results reveal how risk dynamics have changed over the past two decades. Our research may serve as a reference in future overseas oil & gas investment risk decision-making, and is also significant to outbound investing, engineering, and service projects. The proper use of risk assessment results can be conducive to potential investors who may invest in potential countries in the future.

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