Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP)

Environmental risk management is an integral part of risk analyses. The selection of different mitigating or preventive alternatives often involve competing and conflicting criteria, which requires sophisticated multi-criteria decision-making (MCDM) methods. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which integrates subjective and personal preferences in performing analyses. AHP works on a premise that decision-making of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. However, AHP involves human subjectivity, which introduces vagueness type uncertainty and necessitates the use of decision-making under uncertainty. In this paper, vagueness type uncertainty is considered using fuzzy-based techniques. The traditional AHP is modified to fuzzy AHP using fuzzy arithmetic operations. The concept of risk attitude and associated confidence of a decision maker on the estimates of pairwise comparisons are also discussed. The methodology of the proposed technique is built on a hypothetical example and its efficacy is demonstrated through an application dealing with the selection of drilling fluid/mud for offshore oil and gas operations.     

Investigating evidential reasoning for the interpretation of microbial water quality in a distribution network

Total coliforms are used as indicators for evaluating microbial water quality in distribution networks. However, total coliform provides only a weak “evidence” of possible fecal contamination because pathogens are subset of total coliform and therefore their presence in drinking water is not necessarily associated with fecal contamination. Heterotrophic plate counts are also commonly used to evaluate microbial water quality in the distribution networks, but they cover even a wider range of organisms. As a result, both of these indicators can provide incomplete and highly uncertain bodies of evidence when used individually. In this paper, it is shown that combing these two sources of information by an appropriate data fusion technique can provide improved insight into microbial water quality within distribution networks. Approximate reasoning methods like fuzzy logic and probabilistic reasoning are commonly used for data fusion where knowledge is uncertain (i.e., ambiguous, incomplete, and/or vague). Traditional probabilistic frameworks like Bayesian analysis, reasons through conditioning based on prior probabilities (which are hardly ever available). The Dempster–Shafer (DS) theory generalizes the Bayesian analysis without requiring prior probabilities. The DS theory can efficiently deal with the difficulties related to the interpretation of overall water quality where the redundancy of information is routinely observed and the credibility of available data continuously changes. In this paper, the DS rule of combination and its modifications including Yager’s modified rule, Dubois–Prade disjunctive rule and Dezert–Smarandache rule are described using an example of microbial water quality in a distribution network.     

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

Comparative analysis of fuzzy inference systems for water consumption time series prediction

Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP)

Analytic hierarchy process (AHP) is a utility theory based decision-making technique, which works on a premise that the decision-making of complex problems can be handled by structuring them into simple and comprehensible hierarchical structures. However, AHP involves human subjective evaluation, which introduces vagueness that necessitates the use of decision-making under uncertainty. The vagueness is commonly handled through fuzzy sets theory, by assigning degree of membership. But, the environmental decision-making problem becomes more involved if there is an uncertainty in assigning the membership function (or degree of belief) to fuzzy pairwise comparisons, which is referred to as ambiguity (non-specificity). In this paper, the concept of intuitionistic fuzzy set is applied to AHP, called IF-AHP to handle both vagueness and ambiguity related uncertainties in the environmental decision-making process. The proposed IF-AHP methodology is demonstrated with an illustrative example to select best drilling fluid (mud) for drilling operations under multiple environmental criteria.     

Analysis of reservoir water quality using fuzzy synthetic evaluation

A general methodology for fuzzy synthetic evaluation is developed and illustrated with a case study of trophic status assessment for Fei-Tsui Reservoir in Taiwan. The historical data base was collected from the management agency of Fei-Tsui Reservoir from 1987 to 1996. In fuzzy synthetic evaluation, the classification is determined by a matrix operation of the weighted vector with the fuzzy evaluation matrix. After all individual membership functions of evaluated factors have been determined, the fuzzy evaluation matrix can be established. The weighted vector is determined by the analytic hierarchy process method (AHP). The results of this investigation show that the long-term change of water quality and the overturn phenomena cannot be observed with the Carlson index from 1987 to 1992 but is expressed by fuzzy synthetic evaluation. Fuzzy synthetic evaluation is better suited than the Carlson index to rating the trophic status of self-sustaining lakes. Interpretation of the results can provide valuable information to decision makers and aid reservoir management.     

Assessing environmental risks through fuzzy parameterized probabilistic analysis

A fuzzy parameterized probabilistic analysis (FPPA) method was developed in this study to assess risks associated with environmental pollution-control problems. FPPA integrated environmental transport modeling, fuzzy transformation, probabilistic risk assessment, fuzzy risk quantification into a general risk assessment framework, and was capable of handling uncertainties expressed as fuzzy-parameterized stochastic distributions. The proposed method was applied to two environmental pollution problems, with one being about the point-source pollution in a river system with uncertain water quality parameters and the other being concerned with groundwater contaminant plume from waste landfill site with poorly known contaminant physical properties. The study results indicated that the complex uncertain features had significant impacts on modeling and risk-assessment outputs; the degree of impacts of modeling parameters were highly dependent on the level of imprecision of these parameters. The results also implied that FPPA was capable of addressing vagueness or imprecision associated with probabilistic risk evaluation, and help generate risk outputs that could be elucidated under different possibilistic levels. The proposed method could be used by environmental managers to evaluate trade-offs involving risks and costs, as well as identify management solutions that sufficiently hedge against dual uncertainties.     

Risk management of BTEX contamination in ground water--an integrated fuzzy approach

An integrated fuzzy simulation-assessment method (FSAM) was developed for assessing environmental risks from petroleum hydrocarbon contamination in ground water. In the FSAM, techniques of fuzzy simulation and fuzzy risk assessment were coupled into a general framework to reflect a variety of system uncertainties. A petroleum-contaminated site located in western Canada was selected as a study case for demonstrating applicability of the proposed method. The risk assessment results demonstrated that system uncertainties would significantly impact expressions of risk-level outputs. A relatively deterministic expression of the risks would have clearer representations of the study problem but may miss valuable uncertain information; conversely, an assessment under vaguer system conditions would help reveal potential consequences of adverse effects but would suffer from a higher degree of fuzziness in presenting the modeling outputs. Based on the risk assessment results, a decision analysis procedure was used to calculate a general risk index (GRI) to help identify proper responsive actions. The proposed method was useful for evaluating risks within a system containing multiple factors with complicated uncertainties and interactions and providing support for identifying proper site management strategies.     

基于Sugeno型模糊推理的震中烈度预测模型

以Sugeno型模糊推理系统为基础,建立了以震级和震源深度为输入,以震中烈度为输出的震中烈度预测模型,并以四川地区震例数据为例,对模型构建的关键环节进行了详细的说明.通过与参考文献中的拟合模型进行预测数据对比分析,可得本文的推理预测模型精度更高,误差更小,且有更强的扩展性.     

Valuation of in-stream water quality improvement via fuzzy contingent valuation method

Society benefits from rivers in many aspects. To the extent of water resources management, one of the salient issues is that the social benefit of in-stream water quality improvements is often difficult to be quantified for possible cost justification in many water pollution control programs. The difficulties arise from that many service flows of water quality are not channelled through the market system to consumers and producers. With different socio-economic structures, such valuation could be even more challenging when taking river basins with low-income level into account. Recent advances in fuzzy set theory provide a germain insight to viewing the in-stream water quality as a kind of fuzzy resource due to varying awareness of the quality of life. This paper provides a technical analysis using the fuzzy contingent valuation mothod (FCVM) to value in-stream water quality improvements in terms of three fuzzy resources from aesthetic to recreational, and to ecological aspects. Traditional CVM may allow interest groups or affected parties to join and present a more flexible asset assessment with respect to the prescribed environmental features in the river corridor. Yet the FCVM provides a mechanism that lies in providing a mapping (via fuzzy set theory) from a survey of respondents valuation of subjective assessments of water quality into objective economic measures in terms of water quality parameters that management can more directly manipulate. With this new tool, the traditional CVM assessment outputs in a well-developed river basin may even lead to derive a simular valuaton function in a form of a regression equation in a developing river basin where the incme level is relatively low. As part of the sustainablity analysis basin wide, a case study in Taiwan showed that such effort may provide supportive information for cost benefit analysis in many water pollution control programs corresponding to different temporal and spatial scales.     

Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP

On the basis of the disaster system theory and comprehensive analysis of flood risk factors, including the hazard of the disaster-inducing factors and disaster-breeding environment, as well as the vulnerability of the hazards-bearing bodies, the primary risk assessment index system of flood diversion district as well as its assessment standards were established. Then, a new model for comprehensive flood risk assessment was put forward in this paper based on set pair analysis (SPA) and variable fuzzy sets (VFS) theory, named set pair analysis-variable fuzzy sets model (SPA-VFS), which determines the relative membership degree function of VFS by using SPA method and has the advantages of intuitionist course, simple calculation and good generality application. Moreover, the analytic hierarchy process (AHP) was combined with trapezoidal fuzzy numbers to calculate the weights of assessment indices, thus the weights for flood hazard and flood vulnerability were determined by the fuzzy AHP procedure, respectively. Then SPA-VFS were applied to calculate the flood hazard grades and flood vulnerability grades with rank feature value equation and the confidence criterion, respectively. Under the natural disasters risk expression recommended by the Humanitarian Affairs Department of United Nations, flood risk grades were achieved from the flood hazard grades and flood vulnerability grades with risk grade classification matrix, where flood hazard, flood vulnerability and flood risk were all classified into five grades as very low, low, medium, high and very high. Consequently, integrated flood risk maps could be carried out for flood risk management and decision-making. Finally, SPA-VFS and fuzzy AHP were employed for comprehensive flood risk assessment of Jingjiang flood diversion district in China, and the computational results demonstrate that SPA-VFS is reasonable, reliable and applicable, thus has bright prospects of application for comprehensive flood risk assessment, and moreover has potential to be applicable to comprehensive risk assessment of other natural disasters with no much modification.     

Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective

In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis, to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment, risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and the results are discussed comparatively.     

Grey fuzzy optimization model for water quality management of a river system

A grey fuzzy optimization model is developed for water quality management of river system to address uncertainty involved in fixing the membership functions for different goals of Pollution Control Agency (PCA) and dischargers. The present model, Grey Fuzzy Waste Load Allocation Model (GFWLAM), has the capability to incorporate the conflicting goals of PCA and dischargers in a deterministic framework. The imprecision associated with specifying the water quality criteria and fractional removal levels are modeled in a fuzzy mathematical framework. To address the imprecision in fixing the lower and upper bounds of membership functions, the membership functions themselves are treated as fuzzy in the model and the membership parameters are expressed as interval grey numbers, a closed and bounded interval with known lower and upper bounds but unknown distribution information. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for different membership functions, specified for different imprecise goals are interval grey numbers in place of a deterministic real number. In the final solution optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. Application of the GFWLAM is illustrated with case study of the Tunga–Bhadra river system in India.     

Lee生物光学模型在不同水体组分特性下的适用性

辐射传输模型和生物光学模型均可用于模拟水体遥感反射率.前者模拟精度高,但计算复杂.不利于水质参数的反演;后者简便易反演,但在浑浊水体中的模拟精度还有待进一步检验.本文通过设计大量不同组分浓度组成的水体,以辐射传输模型(即Hydrolisht模型)模拟结果为真值,对生物光学模型(即Lee模型)模拟二类水体遥感反射率的精度...     

Integrated risk assessment of urban water supply systems from source to tap

Urban water supply systems (UWSS) are generally composed of water sources, transmission pipes, treatment plants, and distribution networks from source to tap and usually are exposed to variety of uncertain threatening hazards. These threats can be divided to three main groups of natural, human-made, and operational hazards which affect either water quantity or water quality. In order to evaluate the reliability of water supply systems, risk assessment tools must be used to identify threats, their probability, and consequences and vulnerabilities of each element of these systems against the hazards. Due to the complexity and uncertainties affecting water supply systems and threatening hazards, a comprehensive and effective risk assessment method is required. In this study, an integrated fuzzy hierarchical risk assessment model for water supply systems (IFHRA-WSS) is proposed to assess hazards in a complex UWSS using a systematic approach incorporating both water quantity and quality issues. This model uses a hierarchical framework for breaking down the UWSS infrastructures to their interrelated elements to reduce the overall complexity of the system. It also considers uncertainties using Fuzzy Logic approach. Effects of functional interdependencies between different components of the system have also been considered in the vulnerability analysis. IFHRA-WSS incorporates the contributions of urban water experts in a group risk assessment procedure in a way that they can be easily expressed in terms of the qualitative and quantitative risk measures. Efficiency of this model has been examined in a case study which includes a large part of a drinking water supply system in a major city in Iran. This system includes all the elements of the UWSS from the delivery point to the consumption point. In the case study, different components and subcomponents of this system have been ranked based on their estimated risk values. It is envisaged that the results of the proposed model can help the decision makers to plan for effective risk mitigation measures.     

Minimizing the impacts of contaminant intrusion in small water distribution networks through booster chlorination optimization

Contaminant intrusion in a water distribution network (DN) has three basic pre-conditions: source of contaminant (e.g., leaky sewer), a pathway (e.g., water main leaks), and a driving force (e.g., negative pressure). The impact of intrusion can be catastrophic if residual disinfectant (chlorine) is not present. To avoid microbiological water quality failure, higher levels of secondary chlorination doses can be a possible solution, but they can produce disinfectant by-products which lead to taste and odour complaints. This study presents a methodology to identify potential intrusion points in a DN and optimize booster chlorination based on trade-offs among microbiological risk, chemical risk and life-cycle cost for booster chlorination. A point-scoring scheme was developed to identify the potential intrusion points within a DN. It utilized factors such as pollutant source (e.g., sewer characteristics), pollution pathway (water main diameter, length, age, and surrounding soil properties, etc.), consequence of contamination (e.g., population, and land use), and operational factors (e.g., water pressure) integrated through a geographical information system using advanced ArcMap 10 operations. The contaminant intrusion was modelled for E. Coli O156: H7 (a microbiological indicator) using the EPANET-MSX programmer’s toolkit. The quantitative microbial risk assessment and chemical (human health) risk assessment frameworks were adapted to estimate risk potentials. Booster chlorination locations and dosages were selected using a multi-objective genetic algorithm. The methodology was illustrated through a case study on a portion of a municipal DN.     

基于主客观赋权模糊综合评价法的湖泊水生态系统健康评价

运用湖泊营养状态指数判断湖泊的富营养化状态,并根据湖泊的水质、沉积物和水生生物群落的现状和特点,运用主观赋权法中的层次分析法和客观赋权法中的熵权法结合模糊综合评价法,对长江中游地区江汉湖群37个湖泊的水生态系统进行健康状态评价.对湖泊富营养化的调查结果表明,海口湖处于中营养状态,18个湖泊处于富营养化状态,18个湖泊处于超富营养化状态.湖泊生态系统健康评价的研究结果表明,37个湖泊中,处于健康状况"优"的湖泊只有海口湖,处于健康状况"良"的湖泊有5个,分别为东西汊湖、花马湖、梁子湖、童家湖和涨渡湖,其余31个湖泊均处于健康状况"差"的状态.经过与湖泊营养状态指数的对照,本研究结果表明,由主观赋权的专家评分的层次分析法结合模糊综合评价法对江汉湖群湖泊水生态健康状态的评价效果相比客观赋权的熵权模糊综合评价法更贴合实际.     

Well vulnerability: a quantitative approach for source water protection

The concept of vulnerability of drinking water sources is reviewed, and a quantitative approach for assessing well vulnerability for complex three-dimensional ground water systems is developed. The approach focuses on the relative expected impact of potential contaminant sources at unknown locations within a well capture zone, providing relative measures of intrinsic well vulnerability, including the expected times of arrival of a contaminant, the dispersion-related reduction in concentration, the time taken to breach a certain quality objective, and the corresponding exposure times. Thus, the result of the analysis includes the usual advective travel time information used in conventional wellhead protection analysis, plus a set of selected quantitative measures expressing the expected impact. The technique is based on adjoint theory and combines forward- and backward-in-time transport modeling using a standard numerical flow and transport code. The methodology is demonstrated using the case study of a complex glacial multiaquifer system in Ontario. The new approach will be useful in helping water managers develop more physically based and quantitative wellhead protection strategies.     

Assessment of potential strength of an induced earthquake by using fuzzy multifactorial evaluation

The potential strength of an induced earthquake depends on a series of factors at different levels. It can be effectively assessed and predicted by using the method of fuzzy multifactorial evaluation from a fuzzy set theory. As an illustration of the above-mentioned method, this paper has applied the method to assessing the potential strength of induced earthquakes due to water reservoir.