A framework for resilience assessment in process systems using a fuzzy hybrid MCDM model

Resilience engineering (RE) has recently emerged as a novel safety management paradigm in socio-technical organizations. It is believed that RE is more compatible with the characteristics of complex socio-technical systems. The multicriteria nature and the presence of both qualitative and quantitative latent factors make RE substantially more complex especially in quantifying and modeling aspects. To address this issue, the present research aims to develop a fuzzy hybrid multicriteria decision-making (MCDM) model for quantifying and evaluating resilience using the fuzzy Analytic hierarchy process (F-AHP) and fuzzy VIKOR (F-VIKOR) techniques. Initially, an evaluation framework including six resilience indicators and 43 sub-indicators was established. Afterward, the F-AHP method was used to determine the weight of the resilience indicators, while the F-VIKOR method was employed to rank the resilience performance of the different operational units. To present the model capability, we evaluated the resilience of a gas refinery as a typical instance of socio-technical systems. The findings revealed the performance level of resilience indicators in all units of the studied refinery and their ranking based on the computation of the index value (Qi). With respect to the Qi values, the best and worst performance of units from the resilience perspective was specified. Results indicate that the proposed model can serve as an effective evaluation approach in complicated systems and can be used to effectively design strategies to improve system safety performance. To the best of our knowledge, this is the first study that evaluates the resilience using the VIKOR and AHP in a fuzzy environment in the process industry.     

A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields

A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.     

TWIN: a TWo-stage INdex to evaluate ecological quality status in lagoon environments by means of hard bottom zoobenthos

The macrobenthos colonizing the artificial hard bottoms in lagoons has been used in biomonitoring programs as it requires easy sampling procedures and provides a variety of responses to different environmental pressures, like marine or continental influence, eutrophication, and urban pollution. In this paper we present the development and application of a new TWo-stage INdex (TWIN), aimed to assess water quality of lagoon environments using the hard bottom macrobenthos community. TWIN is calculated in two separate stages. First, the presence and abundance of macrobenthic species are used to define, through a fuzzy model, a station's membership grade in six pre-defined ecological sectors (Lagoon Mouth, Vivified Lagoon, Rough Eutrophic, Calm Eutrophic, Urban, Estuarine), each corresponding to characteristic communities. Then a formula links the membership grades to the five ecological status classes ranging from high to bad quality, as requested by the European Water Framework Directive. This method was tested in four Adriatic lagoons: the results are consistent with literature information and expert judgement, therefore we propose its use in the definition of water quality in lagoons.     

Prevalence-adjusted optimisation of fuzzy models for species distribution

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.     

基于模糊综合模型的湿地沉积物重金属污染评价

应用模糊综合-加权模型,以及温州市3个典型湿地沉积物中7种重金属含量数据,对温瑞塘河重金属污染进行评价。模糊综合评价结果表明,温州市居民区河流三垟湿地沉积物属于Ⅴ级重金属污染水平,主要污染元素为Zn和Cd;山前上游河段卧龙河沉积物为Ⅲ级污染,主要污染物是Cd。工业区河流牛桥底河沉积物虽然也是Ⅴ级,但是Cu、Cr、Ni、Zn和Cd 5种重金属污染分值为1.0,小于前2个典型河段的1.2和3.3。内梅罗指数法和潜在生态危害系数法评价结果显示,沉积物的重金属污染程度为牛桥底河三垟湿地卧龙河,这与模糊综合评价结果相符。     

模糊数学法在回顾性环境影响评价中的应用研究

应用模糊综合判别的方法，将工程对影响的多重因素进行综合分析评判，并结合胜利发电厂一期工程的大气回顾性环境影响评价对该方法实用性进行研究探讨。     

劳动河水质的模糊综合评价及分析

利用劳动河8个点位的水质监测数据,以地面水环境质量为标准,运用模糊数学方法,分析劳动河各点位污染状况,综合评价劳动河水质污染程度,确定主要污染点位及主要污染因子,为劳动河污染控制提供科学依据。     

A Complex Systems Model Approach to Quantified Mineral Resource Appraisal

For federal and state land management agencies, mineral resource appraisal has evolved from value-based to outcome-based procedures wherein the consequences of resource development are compared with those of other management options. Complex systems modeling is proposed as a general framework in which to build models that can evaluate outcomes. Three frequently used methods of mineral resource appraisal (subjective probabilistic estimates, weights of evidence modeling, and fuzzy logic modeling) are discussed to obtain insight into methods of incorporating complexity into mineral resource appraisal models. Fuzzy logic and weights of evidence are most easily utilized in complex systems models. A fundamental product of new appraisals is the production of reusable, accessible databases and methodologies so that appraisals can easily be repeated with new or refined data. The data are representations of complex systems and must be so regarded if all of their information content is to be utilized.The proposed generalized model framework is applicable to mineral assessment and other geoscience problems. We begin with a (fuzzy) cognitive map using (+1,0,–1) values for the links and evaluate the map for various scenarios to obtain a ranking of the importance of various links. Fieldwork and modeling studies identify important links and help identify unanticipated links. Next, the links are given membership functions in accordance with the data. Finally, processes are associated with the links; ideally, the controlling physical and chemical events and equations are found for each link. After calibration and testing, this complex systems model is used for predictions under various scenarios. Published on line     

泥石流危险等级评价的模糊数学方法

本文在文献[1]泥石流危险度研究的基础上.提出泥石流危险性评价的模糊数学方法。文中首先根据有关资料建立了评定因子的隶属函数以及各因子在评定泥石流危险性中权重的模糊子集,并在此基础上运用模糊综合评判技术提出了确定泥石流危险等级模糊向量的方法。     

Potential impact model to assess agricultural pressure to landscape ecological functions

Conservation and protection of soil and water resources and visual aspects of landscape, as well as the promotion of biodiversity, are some of the central tasks of environmental policy development and social politics in the future. One of the main questions is: ‘which agricultural systems are able to guarantee sustained resource-conserving land use?’ Based on the ecological risk concepts of the 1970s and 1980s, a potential impact model was developed using a universal assessment algorithm derived from fuzzy logic. The model estimated the potential impact of agricultural land use on ecosystem function using a few resource indicators. Intervention intensities of agricultural land-use are set in relation to site conditions and aggregated for each of several defined potential impact categories. The interpretation with respect to risk and the calculation of potential impact values are explained.