A two-step approach to investigate the effect of rating curve uncertainty in the Elbe decision support system

For river basin management, the reliability of the rating curves mainly depends on the accuracy and time period of the observed discharge and water level data. In the Elbe decision support system (DSS), the rating curves are combined with the HEC-6 model to investigate the effects of river engineering measures on the Elbe River system. In such situations, the uncertainty originating from the HEC-6 model is of significant importance for the reliability of the rating curves and the corresponding DSS results. This paper proposes a two-step approach to analyze the uncertainty in the rating curves and propagate it into the Elbe DSS: analytic method and Latin Hypercube simulation. Via this approach the uncertainty and sensitivity of model outputs to input parameters are successfully investigated. The results show that the proposed approach is very efficient in investigating the effect of uncertainty and can play an important role in improving decision-making under uncertainty.

A cloud-based fuzzy approach for spatial site selection in decision support system

In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.

Fuzzy interval linguistic sets with applications in multi-attribute group decision making

Uncertain and hesitant information, widely existing in the real-world qualitative decision making problems, brings great challenges to decision makers. Hesitant fuzzy linguistic term sets(HFLTSs), an effective linguistic computational tool in modeling and eliciting such information, have hence aroused many scholars’ interests and some extensions have been introduced recently.However, these methods are based on the discrete linguistic term framework with the limited expression domain, which actually depict qualitative information using several single values. Therefore,it is hard to ensure the integrity of the semantics representation and the accuracy of the computation results. To deal with this problem, a semantics basis framework called complete linguistic term set(CLTS) is designed, which adopts a separation structure of linguistic scale and expression domain, enriching semantics representation of decision makers. On this basis the concept of fuzzy interval linguistic sets(FILSs) is put forward that employs the interval linguistic term with probability to increase the flexibility of eliciting and representing uncertain and hesitant qualitative information. For practical applications, a fuzzy interval linguistic technique for order preference by similarity to ideal solution(FILTOPSIS) method is developed to deal with multi-attribute group decision making(MAGDM) problems. Through the cases of movie and enterprise resource planning(ERP) system selection, the effectiveness and validity of the proposed method are illustrated.

A group decision model for credit granting in the financial market

Group decision models that contemplate the particularities of the decision-making process help organizations pursue their strategic objectives.In the financial market,the primary interest of organizations consists in ensuring financial returns,which guarantee stability for the organization.This study identifies major problems in the current process of credit granting in the financial market and argues the need for automatizing the organizational decision process while respecting the autonomy of decision-makers.To this end,this study proposes a group decision model based on the Strategic Choice Approach(SCA)for granting credit in a financial market organization.The results show that the adoption of the proposed model offers considerable gains in terms of organizational goals,transparency of the decision-making process,security for decision-makers,and reduction of organizational conflicts.

A problem solving framework for group decision support system

A new problem solving framework for group decision support system using layer model approach is proposed. This kind of framework includes four basic layers, namely, application layer, task layer, logical layer and physical layer. Based on indicating the respective meanings of those layers a task skeleton of group decision support system and a logical structure of group decision support system generator are put forward and discussed in detail. The framework provides theoretical guidance for developing group decision support system to lower systematic development complexity and support reuse of software.

Negotiated Complexity: Framing Multi-Criteria Decision Support in Environmental Health Practice

The complexity we take into account when dealing with complex issues and the way we deal with that complexity is not given or self-evident, it is framed and negotiated. Based on two environmental health decision support case studies we address a set of key methodological choices, crucial in shaping the multi-criteria decision support and illuminate how they followed from transdisciplinary collaboration and negotiation: diversity tolerance, dealing with uncertainty and difference of opinion, weight of information and the epistemological divide between traditional closed and alternative open paradigms. The case studies exemplify the growing conviction amongst methodologists that, especially regarding complex issues, best methods do not exist as such: methods are chosen and tailored in practice and the quality to a large extent is dependent on the process in which methodological development is embedded. We hope to contribute to making explicit the importance of methodological decision making regarding environmental health complexity.

Fuzzy Set Based Models and Methods of Decision Making and Power Engineering Problems

The results of research into the use of fuzzy set based models and methods of multicriteria decision making for solving power engineering problems are presented. Two general classes of models related to multiobjective (X,M> models) and multiattribute (X,R> models) problems are considered. The analysisX,M> of models is based on the use of the Bellman-Zadeh approach to decision making in a fuzzy environment. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. Several techniques based on fuzzy preference modeling are considered for the analysis of?X,R> models. A review of the authors’ results associated with the application of these models and methods for solving diverse types of problems of power system and subsystems planning and operation is presented. The recent results on the use ofX,M> andX,R> models and methods of their analysis for the allocation of reactive power sources in distribution systems and for the prioritization in maintenance planning in distribution systems, respectively, are considered.

Applying an Ordinal Priority Approach Based Neutrosophic Fuzzy Axiomatic Design Approach to Develop Sustainable Geothermal Energy Source

Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.

A stochastic approach for integrating market and technical uncertainties in economic evaluations of petroleum development

The paper presents a stochastic and economic analysis for petroleum development under uncertain market and technical environments. Mean-reversion with jumps for price forecasting is used to consider market uncertainty, while various scenarios for the reservoir properties and cost are employed to consider technical uncertainty. Monte Carlo simulation is carried out to obtain the feasible range of net present values and internal rates of return. The influence of stochastic parameters is examined through correlation coefficients. The stochastic approach yields more reliable evaluation and effectively investigates the characteristics of development. The integration of uncertainties and contractual terms results in an irregular tendency in the future cash flow and reveals that a larger reserve does not guarantee a greater profit. The reserve and the well rate affect the economic values whereas the parameters for price prediction don＇t. The research confirms the necessity of qualifying uncertainties for realistic decision-making at the initial stage of development.

Dominance-based rough set approach as a paradigm of knowledge discovery and granular computing

Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.

A New Approach to Intelligent Model Based Predictive Control Scheme

This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.

Development of a dynamic operational rating system in energy performance certificates for existing buildings： Geostatistical approach and data-mining technique

The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system,which can be subdivided into three aspects:(i) building category;(i i) region category; and(iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating(DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics.To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system(i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.

Fuzzy VIKOR Approach to Evaluate the Information Security Policies and Analyze the Content of Press Agencies in Gulf Countries

A news agency is an organization that gathers news reports and sells them to subscribing news organization, such as newspapers, magazines, radio and television broadcasters. A news agency may also be referred to as a wire service, newswire, or news service. The main purpose of this paper is to evaluate the security policies and analyze the content of five press agencies in gulf countries which are (Kuwait News Agency (KUNA), Emirates News Agency (WAM), Saudi Press Agency (SPA), Bahrain News Agency (BNA), and Oman News Agency (OMA)) by using a fuzzy VIKOR approach where linguistic variables are applied to solve the uncertainties and subjectivities in expert decision making. Fuzzy VIKOR approach is one of the best Multi-Criteria Decision Making (MCDM) techniques working in fuzzy environment. This study benefits security and content analysis experts know which press agency has the mandate and the competence to educate the public on news agencies. Besides, this paper contributes to Gulf agencies in helping them in their resolve to ensure the quality of content information and information security policies over the internet.

Dynamic probability evaluation of safety levels of earth-rockfill dams using Bayesian approach

In order to accurately predict and control the aging process of dams, new information should be collected continuously to renew the quantitative evaluation of dam safety levels. Owing to the complex structural characteristics of dams, it is quite difficult to predict the time-varying factors affecting their safety levels. It is not feasible to employ dynamic reliability indices to evaluate the actual safety levels of dams. Based on the relevant regulations for dam safety classification in China, a dynamic probability description of dam safety levels was developed. Using the Bayesian approach and effective information mining, as well as real-time information, this study achieved more rational evaluation and prediction of dam safety levels. With the Bayesian expression of discrete stochastic variables, the a priori probabilities of the dam safety levels determined by experts were combined wfth the likelihood probability of the real-time check information, and the probability information for the evaluation of dam safety levels was renewed. The probability index was then applied to dam rehabilitation decision-making. This method helps reduce the difficulty and uncertainty of the evaluation of dam safety levels and complies with the current safe decision-making regulations for dams in China. It also enhances the application of current risk analysis methods for dam safety levels.

Interactive multiobjective optimization using dominance-based rough set approach

We present main ideas of a recently proposed method for interactive multiobjective optimization,which is based on application of a logical preference model built using the Dominance-based Rough Set Approach(DRSA).

基于文本挖掘的水电工程施工物体打击事故致因分析

为探明水电工程施工过程中物体打击事故的致因及作用关系,提出针对性的事故预防措施,从源头遏制事故发生。遴选水电工程施工物体打击事故调查报告,运用文本挖掘从人、机、环、管4个方面提取14项事故致因。融合关联规则、决策试验和评价实验室(DEMATEL)方法,厘清致因因素间的关联关系,构建重要度分析模型,确定事故近端致因因素。结果表明:水电工程物体打击事故各致因间联系紧密,其中,安全监督检查不到位、违章作业、安全防护措施不完备、安全意识淡薄等致因因素对事故发生具有直接影响作用。现场清理不彻底、安全技术交底不到位、安全教育培训不到位等是诱发事故的深层影响因素,且安全意识淡薄的中心度排名第一,对事故致因系统的影响较大,在实际施工中应着重考虑上述致因因素。

基于类型学方法的城镇住宅节能改造效果的分类评估——以重庆市为例

针对城镇住宅量大面广、复杂多样、能效偏低且参差不齐的现况,基于类型学方法,以重庆市1950—2000年间的城镇住宅为例,采用CHAID决策树算法,按照建造年代、住宅套型和套型组合三种属性,从373例样本中归纳出围护结构的三个大类和24个子类,并构建原型的物理模型。模拟分析表明:分类评估可以更好地解释不同建筑类型冷、热负荷需求的影响因子及其显著程度,筛选出最不节能的类型及各类型最不利于节能的部位;不同类型住宅的冷、热负荷节约率取决于节能改造后不同部位的贡献度。推荐运用CHAID决策树算法对节能改造评价标准进行类型化细分,并扩展至更大区域范围的能效评估和改造潜力预测。

公共卫生决策证据质量的Meta流行病学研究

目的:比较公共卫生决策证据质量评价体系(PHE-Grading)与推荐分级的评价、制定与评估体系(GRADE)在评价公共卫生决策证据质量方面的差异性。方法:计算机检索Cochrane Library数据库,获取建库起至2024年2月27日发表的有关“Public health”主题的系统评价。采用EndNote 20软件进行文献筛选,Excel 2021和SPSS 22.0软件进行数据整理和分析,使用RevMan 5.4.1软件绘制森林图。结果:最终纳入61篇系统评价进行证据质量评价。GRADE和PHE-Grading证据分级结果的森林图显示,证据等级为高[OR:2.39,95%CI(1.21,4.75)]、中[OR:0.40,95%CI(0.31,0.52)]、低[OR:0.37,95%CI(0.29,0.46)]和极低[OR:85.11,95%CI(34.80,208.11)],证据质量分级结果的差异有统计学意义。结论:与GRADE相比,PHE-Grading对公共卫生决策证据确信度的把握可能更为精准,目前公共卫生决策证据质量仍集中在中低水平,仍需加强高质量研究以支持科学决策。

基于权重估计与混合多标准决策的帕累托最优解筛选

对于多目标优化问题,传统的多标准决策方法在求得帕累托最优解集时就会结束。但在实际问题中,往往需要基于决策者的偏好从解集中选取单一的最优方案。基于该问题,创新性地提出基于最小生成树算法的权重学习方法,用于整合决策者的偏好,并结合层次分析法与消除和选择表达现实法的思想,提出一种混合多标准决策方法,帮助选择最符合要求的帕累托最优解。为了验证该方法的可靠性,基于某地区天然气管网销售多目标模型求得的帕累托最优解数据进行测试与分析,结果表明:该方法选出的帕累托最优解能够很好地契合决策者偏好,并为帕累托前沿的选择提供有效的建议。

Pythagorean模糊环境下的三支多属性决策方法

将后悔理论与逼近理想解排序(TOPSIS)法相结合,提出了一种新的三支多属性决策方法,旨在减少决策者的后悔情绪,提高决策满意度.首先,通过评价信息表确定各属性下备选方案的状态集,计算各方案的条件概率.其次,结合TOPSIS和后悔理论,计算相对损失函数,进而确定期望损失,并建立三支决策规则,对方案进行排序并划分为接受域、延迟决策域或拒绝域.最后,以城市轨道交通站点选择为例,进行案例对比分析,验证了该方法的可行性和有效性;接着进行参数分析,以验证该方法的稳定性.