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.

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

2020 ◽  
Vol 27 (8) ◽  
pp. 1813-1833 ◽  
Author(s):  
Wenpei Xu ◽  
Ting-Kwei Wang
Keyword(s):  
Risk Assessment ◽  
Computer Vision ◽  
Expert Knowledge ◽  
Safety Information ◽  
Assessment Model ◽  
Risk Indicators ◽  
Risk Assessment Model ◽  
Content Type ◽  
Risk Levels

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.

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.

Developing a risk assessment model for banking software development projects based on rough-grey set theory

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tooraj Karimi ◽  
Yalda Yahyazade
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Expert System ◽  
Software Development ◽  
Set Theory ◽  
Rough Set ◽  
Risk Level ◽  
Development Projects ◽  
Content Type ◽  
Grey Clustering

PurposeRisk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.Design/methodology/approachIn this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributesFindingsIn this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurateResearch limitations/implicationsIt is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projectsOriginality/valueThe risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

A paratactic subjective-objective weighting methods and SVM risk assessment model applied in textile and apparel safety

2015 ◽  
Vol 32 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Xiongying Wu ◽  
Lihong Chen ◽  
Shuhui Pang ◽  
Xuemei Ding
Keyword(s):  
Risk Assessment ◽  
Empirical Analysis ◽  
Safety Evaluation ◽  
Assessment Model ◽  
Risk Indicators ◽  
Support Vector ◽  
Potential Hazard ◽  
Risk Assessment Model ◽  
Content Type ◽  
Textile And Apparel

Purpose – The purpose of this paper is to explore a descriptive framework for a more structured and objective evaluation of the risk situation of textile and apparel, also to find the best set of methods or optimal scientific grounds for the safety evaluation of textile and apparel. Design/methodology/approach – Risk analysis theory is used to analyze potential hazard of textile and apparel, weight is given to risk indicators using subjective and objective weighting method, respectively, grading standards of safe risk of textile and apparel is made. Finally a safety risk assessment model of textile and apparel based on support vector machine (SVM) is built, and empirical analysis is also made. Findings – Quantitative and highly reliable evaluation of textile and apparel risks, relatively easy grading classification and simplicity in operating the evaluation process are the advantages that promote the application of risk assessment model based on SVM for textile and apparel, and empirical analysis showed considerably good applicability. Practical implications – The research is useful to ensure safety textile and apparel in market, also contributing to the sustainable development of textile industries in future. Originality/value – SVM as a risk assessment method provided safety evaluation to toxic and harmful substance and small parts in textile and apparel, which can be an effective tool to monitor textile and apparel safety.

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.

Influence of factors on collapse risk of loess tunnel: a multi-index assessment model

2018 ◽  
Vol 16 (5) ◽  
pp. 734-749
Author(s):  
Xueliang Zhang ◽  
Meixia Wang ◽  
Binghua Zhou ◽  
Xintong Wang
Keyword(s):  
Risk Assessment ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Surrounding Rock ◽  
Assessment Model ◽  
Burial Depth ◽  
Risk Assessment Model ◽  
Content Type ◽  
Close Time

Purpose Because of the properties of loess, the occurrence of collapse following deformation of a large settlement is a common problem during the excavation of tunnels on loess ground. Hence, risk management for safer loess tunnel construction is of great significance. The purpose of this paper is to explore the influence of factors on collapse risk of loess tunnels and establish a risk assessment model using rough set theory and extension theory. Design/methodology/approach The surrounding rock level, groundwater conditions, burial depth, excavation method and support close time were selected as the factors and settlement deformation was the verification index for risk assessment. First, using rough set theory, the influence of risk factors on the collapse risk of loess tunnels was calculated by researching engineering data of excavated sections. Then, a collapse risk assessment model was developed based on extension theory. As the final step, the model was applied to practical engineering in the Loess Plateau of China. Findings The weights of surrounding rock level, groundwater conditions, burial depth, excavation method and support close time obtained using rough set theory were respectively 10.811 per cent, 18.919 per cent, 24.324 per cent, 40.541 per cent and 5.406 per cent. The assessment results obtained using the model were in good agreement with field observations. Originality/value This study highlights key points in collapse risk management of loess tunnels, which could be very useful for future construction methods. The model, using easily obtained parameters, helps in predicting the collapse risk level of loess tunnels excavated under different geological conditions and by different construction organizations and provides a reference for future studies.

An integrated framework for the assessment of inbound supply risk and prioritization of the risk drivers

2019 ◽  
Vol 27 (3) ◽  
pp. 1261-1286 ◽  
Author(s):  
Ramesh K.T. ◽  
Sarada P. Sarmah ◽  
Pradeep Kumar Tarei
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Supply Chain ◽  
Risk Score ◽  
Service Industries ◽  
Risk Indicators ◽  
Supply Risk ◽  
Content Type ◽  
Indian Context ◽  
The Impact

Purpose The purpose of this paper is to present a framework for identifying various inbound supply-risk factors and analyzing its indicators considering the contextual relationship between them. This study additionally proposes a framework for developing an overall inbound supply-risk score considering a real-life case of the electronics supply chain (ESC) in the Indian context. Design/methodology/approach In total, 32 risk indicators are identified by a systematic literature review approach and are validated by supply chain practitioners/experts and further categorized into six main risk factors. A hybrid multi-criteria decision-making-based DANP (DEMATEL and ANP) framework is employed to develop the overall inbound-supply-risk score (ISRS) and to prioritize the risk indicators. Indian ESC is chosen as a viable case study to demonstrate the effectiveness of the proposed framework. Findings The outcomes from the study reveal that the overall ISRS in the ESC is 36 percent and additionally forewarns critical inbound-supply-risk factors such as supplier performance, product, and buyer organization. Further, the study also identifies the most significant risk indicators such as price margin, investment, on-time delivery, order fulfillment and design changes for ESC. Research limitations/implications Supply chain practitioners can adopt this framework as a useful inbound supply-risk assessment tool. Moreover, the hybrid framework will address subjectivity and interrelations among various factors through experts’ judgments. The results will assist the managers to have better insights on the critical risk factors and their complicated interrelationships and further strategize action plans to nullify the impact of incoming risks. This study mainly focused on risk identification and assessment of electronics inbound-supply-risk indicators in the Indian context. The framework can be used for other manufacturing and service industries, albeit the results derived are in the context of a developing country. Originality/value This paper provides an effective risk assessment framework for the supply chain practitioners/managers to develop a decision-support system for inbound-supply-risk quantification and prioritization of risk factors in the context of the ESC.

scholarly journals Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index

2018 ◽  
Vol 25 (4) ◽  
pp. 534-558 ◽  
Author(s):  
Saeed Akbari ◽  
Mostafa Khanzadi ◽  
Mohammad Reza Gholamian
Keyword(s):  
Prediction Model ◽  
Rough Set ◽  
Construction Projects ◽  
Large Scale ◽  
Construction Project ◽  
Decision Rules ◽  
Scale Construction ◽  
Classification Model ◽  
Content Type ◽  
Success Index

PurposeTo address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.Design/methodology/approachAt first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.FindingsThe results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.Originality/valuePerhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.

scholarly journals Carbon risk real estate monitor: making decarbonisation in the real estate sector measurable

2020 ◽  
Vol 13 (3) ◽  
pp. 277-299
Author(s):  
Maximilian M. Spanner ◽  
Julia Wein
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Real Estate ◽  
Value At Risk ◽  
Risk Indicators ◽  
Valuable Insight ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector

Purpose The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks. Design/methodology/approach The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days. Findings The paper provides property-specific carbon emission pathways, as well as valuable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered. Research limitations/implications The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector. Practical implications The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data. Social implications The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retrofit planning. Originality/value The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways.

scholarly journals Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam

2021 ◽  
Vol 10 (4) ◽  
pp. 198
Author(s):  
Sevim Sezi Karayazi ◽  
Gamze Dane ◽  
Bauke de Vries
Keyword(s):  
Geographically Weighted Regression ◽  
Spatial Clustering ◽  
Least Square ◽  
Geospatial Data ◽  
Evidence Based ◽  
Ordinary Least Square ◽  
Mass Tourism ◽  
Dbscan Algorithm ◽  
National Monuments ◽  
Combined Data

Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam.

Sign in / Sign up

Export Citation Format

Share Document