scholarly journals Recent studies on risks in land use

2013 ◽  
pp. 140-148
Author(s):  
Ivan Kovalchuk
Keyword(s):  
Risk Factors ◽  
Land Use ◽  
Risk Management ◽  
Key Words ◽  
Risk Prediction ◽  
Risk Level ◽  
Land Resources ◽  
Economic Use

The paper explores the kinds of restrictions on use of land resources, describes factors which affect on land use safety, the classification of risks that accompany economic use of land resources. Risks of land use were set in 8 blocks, each of them divided on 5 to 16 types of risks. Determination of the risk level of land use should be implemented as a series of steps - inventory of land, analytical and evaluative studies of risk prediction and visualization of the results and implementation of proposals aimed at reducing the risks of land use. Key words: land resources, land use risks, risk factors, risk management in land use.

scholarly journals Productive soils as basis for effective agricultural land use in Carpathian region of Ukraine

2013 ◽  
pp. 257-264 ◽  
Author(s):  
Zinovii Pankiv
Keyword(s):  
Land Use ◽  
Key Words ◽  
Economic Performance ◽  
Agricultural Land ◽  
Arable Land ◽  
Integrated Approach ◽  
Agricultural Land Use ◽  
Land Resources ◽  
Carpathian Region ◽  
Definition Of

The structure of soils in Carpathian region of Ukraine, which is involved for agricultural land use, including for arable land, has been analyzed. The basic steps to increase the area of arable land have been described. Integrated approach with using spatial, legal, environmental, and economic performance has been proposed for definition of the efficiency of agricultural land use. The appellation “soil use” for characteristic the type using of agricultural land resources has been proposed. Key words: productive soils, land use, the Carpathian region of Ukraine, soil use

scholarly journals ABC Classification of Risk Factors in Production Supply Chains with Uncertain Data

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Aleksandar Vujović ◽  
Aleksandar Đorđević ◽  
Ranka Gojković ◽  
Milan Borota
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Supply Chain ◽  
Good Practice ◽  
Uncertain Data ◽  
Real Life ◽  
Frequency Of Occurrence ◽  
Management Problem ◽  
Extreme Risk

The problem of classification of risk factors in an uncertain environment is part of the risk management problem, which has a critical effect on the competitive advantage of production supply chain. The severities of consequences, their relative importance, and the frequency of occurrence of risk factors are defined by risk management team, depending on their experience and the results of good practice. Fuzzy rating of the severities of consequences and the frequency of occurrence of risk factors are described by linguistic expressions, which are modeled by triangular fuzzy numbers. The risk values, obtained by the materialization of the identified risk factors, are given precisely with the usage of fuzzy algebra rules. The classification criterion is defined as the distance between current risk value and extreme risk values. The proposed model enables determination of the priorities of risk factors. It is illustrated by an example with real-life data from a production supply chain in auto industry.

The Risk Management of Reducing Merger and Acquisitions Risks for Construction Enterprise

2014 ◽  
Vol 584-586 ◽  
pp. 2359-2362
Author(s):  
Fang Jing Ma
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Potential Risk ◽  
Merger And Acquisition ◽  
Risk Level ◽  
Construction Enterprise ◽  
Construction Enterprises ◽  
Potential Risk Factors ◽  
Potential Risks ◽  
Merger And Acquisitions

In order to study the Merger and Acquisition (M&A) risk management for construction enterprise with the Hierarchical Holographic Modeling (HHM) is used to identify potential risks during different phases of M&A. In addition, Risk Filtering, Ranking and Management, which is short for RFRM, can also be used as a tool of risk level assessment to find more effective risks and find the suitable solutions. If M&A is used to identify the potential risks and analyze the risk factors of construction enterprises, they can be very helpful for finding out the solutions for the potential risk factors. At the same time it is useful for managing the M&A project potential risks and implementing well to increase the successful rate.

scholarly journals Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 172
Author(s):  
Debalina Banerjee Chattapadhyay ◽  
Jagadeesh Putta ◽  
Rama Mohan Rao P
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Risk Management ◽  
High Risk ◽  
Risk Prediction ◽  
Learning Model ◽  
Risk Identification ◽  
Large Set ◽  
High Risk Factors ◽  
Machine Learning Model

Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA–K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success.

Risk of Ethical Financial Investments of Firms

2020 ◽  
Vol XVI ◽  
pp. 59-77
Author(s):  
Leszek Kędzierski
Keyword(s):  
Risk Factors ◽  
Risk Classification ◽  
Risk Measurement ◽  
Measurement Process ◽  
Finance Management ◽  
Ethical Finance ◽  
Financial Investments

Ethical tangible, intagible and financial investments are realized in the framework of ethical finance management of firms. Ethical financial in-vestments projects by kinds are accompanied by different kinds of risk. Risk measurement process of these investments includes: classification of risk, classification of risk factors and determination of risk size using different methods.

The Research and Management Strategies of Supply Chain Risks

2014 ◽  
Vol 644-650 ◽  
pp. 5523-5527
Author(s):  
Ran Ding
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Supply Chain ◽  
Management Strategies ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Future Studies ◽  
Countermeasures And Suggestions ◽  
Supply Chain Risks

The competition between supply chains is increasingly fierce on the current, supply chain risk management is got more attention by people. This paper elaborates definitions and meaning of the supply chain risk and supply chain risk management at first, followed by the classification of supply chain risk factors whose features are analyzed and compared thereafter. In the last, the paper proposes countermeasures and suggestions on supply chain risk management, which provides a useful reference for the future studies.

scholarly journals ОСОБЕННОСТИ УПРАВЛЕНИЯ ИНВЕСТИЦИОННЫМИ РИСКАМИ В СТРОИТЕЛЬСТВЕ НКР

2019 ◽  
pp. 15-21
Author(s):  
Анжела Алешаевна Барсегян
Keyword(s):  
Risk Management ◽  
Public Relations ◽  
Structural Changes ◽  
Geographical Location ◽  
Cost Effective ◽  
Risk Level ◽  
Construction Sector ◽  
Natural Risk ◽  
Risk Management Process

Recent structural changes in the economy of the Nagorno-Karabakh Republic have had a significant impact on the entire public relations system, obliging to review the mechanisms for making economic decisions as primary ones. The formation of NKR coincided with the collapse of the USSR and with the radical changes in the system of economic relations that led to this. Since its formation, local authorities have adopted a strategy for the development of the construction sector by stimulating investment, which intends the creation of a favorable investment environment and the development of an appropriate infrastructure. Despite the fact that NKR construction sector has great potential for attracting investments, the level of foreign investors’ activity in this area is quite low. The main reason is probably a high risk level, which is determined by such factors as political instability in the region, the geographical location of NKR, its economic isolation, an incomplete legal framework, and the underdevelopment of financial and market infrastructures. As a result, many investment projects, including cost-effective and efficient ones, are not implemented.Uncertainty associated with the possibility of adverse situations and consequences during the implementation of the project is characterized by the concept of “risk”. The effectiveness of the risk management organization is largely determined by the classification of risk. Depending on the main cause of the risk (basic or natural risk), the classification of risks associated with the implementation of investment construction projects provides a favorable basis for a clear and reasonable risk assessment, which is one of the crucial factors in the effectiveness of the risk management process.

scholarly journals Methodical backgrounds and data base of exploring land use results (on example of Zhytomyr region)

2014 ◽  
pp. 123-136
Author(s):  
Z. Tarnavska-Teterska
Keyword(s):  
Land Use ◽  
Key Words ◽  
Data Base ◽  
Agricultural Land ◽  
Land Resources ◽  
Environmental Aspects ◽  
Environmental Consequences ◽  
Geographical Study ◽  
Effects Of Land Use ◽  
Land Economic

This article is about evaluation of land use from the standpoint of economic and environmental consequences of trading in the Zhytomyr region during 2000–2008 years. The methodical approaches are resulted to socio-geographical study on the effects of land use and the factual basis of the study. Key words: land resources, land use, non-agricultural land, economic and environmental aspects, Zhytomyr region.

Fuzzy Swot Analysis for Selection of Bankruptcy Risk Factors

2013 ◽  
Vol 379 ◽  
pp. 207-213 ◽  
Author(s):  
Alexandra A. Zakharova
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Management Strategy ◽  
Implementation Strategy ◽  
Swot Analysis ◽  
Risk Management Strategy ◽  
Internal Factors ◽  
Bankruptcy Risk ◽  
Selection Of

The article discusses applicability of fuzzy SWOT analysis for bankruptcy risk management of companies. The article also provides its implementation strategy, sample determination of external and internal factors’ significance and their combinations for bankruptcy risk management strategy of company.

scholarly journals Development of Score Based Smart Risk Prediction Tool for Detection of Type-1 Diabetes: A Bioinformatics and Machine Learning Approach

2020 ◽  
Vol 11 (2) ◽  
pp. 9007-9016
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 1 Diabetes ◽  
Risk Prediction ◽  
Statistical Approach ◽  
Risk Level ◽  
Prediction Tool ◽  
Machine Learning Approach ◽  
And Control

In this study, a smart risk prediction tool has been demonstrated along with the algorithm, which works as a backend of the tool to detect Type-1 Diabetes. The algorithm was contrived by the weightage values that are articulated by analyzing the risk factors of Type-1 diabetes. The analysis takes place with a machine learning and statistical approach. Data were collected from a number of cases and control groups, which was preprocessed to be fit for the analysis. Risk factors were extracted by comparing two different approaches one is machine learning, and another is the statistical approach. A common regulatory pattern was found that leads to the design of an algorithm that gives a predictive result of the risk level of any user for Type-1 Diabetes. Elaborated results of different approaches have also been shown in this paper, which gives clear excogitation about risk factors and their ranking.

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