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.     

An interval-valued fuzzy linear programming with infinite ��-cuts method for environmental management under uncertainty

In this study, an interval-valued fuzzy linear programming with infinite α-cuts (IVFLP-I) method is developed for municipal solid waste (MSW) management under uncertainty. IVFLP-I can not only tackle uncertainties expressed as intervals and interval-valued fuzzy sets, but also take all fuzzy information into account by discretizing infinite α-cut levels to the interval-valued fuzzy membership functions. Through adoption of the interval-valued fuzzy sets, IVFLP-I can directly communicate information of waste managers’ confidence levels over various subjective judgments into the optimization process. Compared to the existing methods in which only finite α-cut levels exist, IVFLP-I would have enhanced the robustness in the optimization efforts. A MSW management problem is studied to illustrate the applicability of the proposed method. Four groups of optimal solutions can be obtained through assigning different intervals of α-cut levels. The results indicate that wider intervals of α-cut levels could lead to a lower risk level of constraint violation associated with a higher system cost; contrarily, narrower intervals of α-cut levels could lead to a lower cost with a higher risk of violating the constraints. The solutions under different intervals of α-cut levels can support in-depth analyses of tradeoffs between system costs and constraint-violation risks.     

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte–Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga–Bhadra river system in southern India, with a steady state BOD–DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality.     

基于区间直觉模糊数的地震应急服务点选址模型

地震应急物资储备服务点选址是地震应急救援决策工作的重要基础。本文分析了地震应急服务点选址问题的不确定性,介绍了直觉模糊数和区间直觉模糊数的概念,在分析两者之间关系的基础上,定义了区间直觉模糊数的得分函数和精确函数,进而提出了基于得分函数和精确函数的区间直觉模糊数的排序规则;行车时间受诸多因素影响,将行车时间看成区间直觉模糊信息,构建了约束中含有区间直觉模糊参数的地震应急服务点选址模型,提出了一种基于区间直觉模糊数排序规则的模型算法,可得到地震应急服务点最优选址方案。通过算例分析验证了该方法的有效性。     

基于自适应区间二型模糊聚类的遥感土地覆盖自动分类

遥感影像土地覆盖分类面临"类别密度差异显著"、"同谱异物"和"同物异谱"等不确定性问题,传统的分类方法(如FCM)因不能描述高阶模糊不确定性,无法完成准确建模,使分类误差较大,而二型模糊集恰是处理此类不确定性的有效工具.在引入二型模糊集新概念和自适应降型新方法的基础上,提出一种自适应二型模糊分类方法(A-IT2FCM):(1)基于样本集模糊距离度量构建面向分类的区间二型模糊集,以尽可能降低对先验知识和预设参数的依赖,从而满足自动分类的要求;(2)给出一种自适应探求等价一型代表(模糊)集合的高效降型方法,在此基础上进行自适应区间二型模糊聚类.实验数据为珠海横琴和北京颐和园的SPOT5影像数据,对比方法有AIT2FCM、基于Karnik-Mendel算法降型和基于Tizhoosh提出的简易降型方法的区间二型模糊C均值聚类以及作者前期研究提出的区间值模糊C-均值算法(IV-FCM).实验结果表明,A-IT2FCM方法分类效果佳,在类别具有较大密度差异和多重模糊性时能得到比FCM及IV-FCM更精确的边界和更连贯的类别,适于处理遥感影像土地覆盖类别的深层不确定性;同时在"光谱混叠"现象严重时,可以获得比对比方法更稳健、精度更高的影像自动分类结果,且时间复杂度明显低于基于Karnik-Mendel方法.     

Fuzzy chance-constrained linear fractional programming approach for optimal water allocation

A fuzzy chance-constrained linear fractional programming method was developed for agricultural water resources management under multiple uncertainties. This approach improved upon the previous programming methods, and could reflect the ratio objective function and multiple uncertainties expressed as probability distributions, fuzzy sets, and their combinations. The proposed approach is applied to an agricultural water resources management system where many crops are considered under different precipitation years. Through the scenarios analyses, the multiple alternatives are presented. The solutions show that it is applicable to practical problems to address the crop water allocation under the precipitation variation and sustainable development with ratio objective function of the benefit and the irrigation amount. It also provides bases for identifying desired agriculture water resources management plans with reasonable benefit and irrigation schedules under crops.     

An inexact fuzzy-random-chance-constrained air quality management model

An inexact fuzzy-random-chance-constrained programming model (IFRCCMM) was developed for supporting regional air quality management under uncertainty. IFRCCMM was formulated through integrating interval linear programming within fuzzy-random-chance-constrained programming framework. It could deal with parameter uncertainties expressed as not only fuzzy random variables but also discrete intervals. Based on the stochastic and fuzzy chance-constrained programming algorithms, IFRCCMM was solved when constraints was satisfied under different satisfaction and violation levels of constraints, leading to interval solutions with different risk and cost implications. The proposed model was applied to a regional air quality management problem for demonstration. The obtained results indicated that the proposed model could effectively reflect uncertain components within air quality management system through employing multiple uncertainty-characterization techniques (in random, fuzzy and interval forms), and help decision makers analyze trade-offs between system economy and reliability. In fact, many types of solutions (i.e. conservative solutions with lower risks and optimistic solutions with higher risks) provided by IFRCCMM were suitable for local decision makers to make more applicable decision schemes according to their understanding and preference about the risk and economy. In addition, the modeling philosophy is general and applicable to many other environmental problems that may be complicated with multiple forms of uncertainties.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

Non-equilibrium alcohol flooding model for immiscible phase remediation: 1. Equation development

The equations for a compositional model for simulation of a two-phase, three-component system with inter-phase mass transfer are developed. Emphasis is placed on development of inter-phase mass transfer equations for incorporation of rate-limited inter-phase mass transfer. Due to the nature of the three-component systems considered, a single-film model may be inadequate and a two-film model must be utilized. A two-film model accounts for the simultaneous transfer of components in both directions across phase interfaces. The effect of interaction between components on diffusion is considered using a general form of Fick's Law. A Hand Plot representation of ternary phase behavior is chosen since it allows for straightforward calculation of miscibility of bulk phases under conditions of local non-equilibrium. The developed set of equations form the basis for a numerical model to simulate the enhanced removal of non-aqueous phase liquids (NAPLs) from porous media using single-component alcohol floods.     

Planning of municipal solid waste management systems under dual uncertainties: a hybrid interval stochastic programming approach

In this study, a random-boundary-interval linear programming (RBILP) method is developed and applied to the planning of municipal solid waste (MSW) management under dual uncertainties. In the RBILP model, uncertain inputs presented as interval numbers can be directly communicated into the optimization process; besides, intervals with uncertain lower and upper bounds can be handled through introducing the concept of random boundary interval. Consequently, robustness of the optimization process can be enhanced. To handle uncertainties with such complex presentations, an integrated chance-constrained programming and interval-parameter linear programming approach (ICCP) is proposed. ICCP can help analyze the reliability of satisfying (or risk of violating) system constraints under uncertainty. The applicability of the proposed RBILP and ICCP approach is validated through a case study of MSW management. Violations for capacity constraints are allowed under a range of significant levels. Interval solutions associated with different risk levels of constraint violation are obtained. They can be used for generating decision alternatives and thus helping waste managers to identify desired policies under various environmental, economic, and system-reliability constraints.     

An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties

In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.     

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties

In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.     

Fast modelling of marine surface multiples

In the development and testing of water‐surface multiple‐removal algorithms, it is valuable to have accurate synthetic seismograms which exhibit multiples, for which the multiple‐free solution is known. A method is presented for constructing 2D and 3D solutions of the acoustic wave equation in water, by combining the solution from a primary source with other scaled solutions of secondary sources, which simulate diffractors. The computation involves function evaluation rather than numerical solution of differential equations and is consequently accurate and comparatively fast. The analytic formulae on which the method is based give insights into methods for multiple removal. Generalized reflection coefficients, defined on a horizontal plane above the diffractors, are derived and used to construct the integral equations which are the basis for many multiple‐removal schemes.     

A double-porosity model for a fractured aquifer with non-Darcian flow in fractures

Abstract

Non-Darcian flow in a finite fractured confined aquifer is studied. A stream bounds the aquifer at one side and an impervious stratum at the other. The aquifer consists of fractures capable of transmitting water rapidly, and porous blocks which mainly store water. Unsteady flow in the aquifer due to a sudden rise in the stream level is analysed by the double-porosity conceptual model. Governing equations for the flow in fractures and blocks are developed using the continuity equation. The fluid velocity in fractures is often too high for the linear Darcian flow so that the governing equation for fracture flow is modified by Forcheimer's equation, which incorporates a nonlinear term. Governing equations are coupled by an interaction term that controls the quasi-steady-state fracture—block interflow. Governing equations are solved numerically by the Crank-Nicolson implicit scheme. The numerical results are compared to the analytical results for the same problem which assumes Darcian flow in both fractures and blocks. Numerical and analytical solutions give the same results when the Reynolds number is less than 0.1. The effect of nonlinearity on the flow appears when the Reynolds number is greater than 0.1. The higher the rate of flow from the stream to the aquifer, the higher the degree of nonlinearity. The effect of aquifer parameters on the flow is also investigated. The proposed model and its numerical solution provide a useful application of nonlinear flow models to fractured aquifers. It is possible to extend the model to different types of aquifer, as well as boundary conditions at the stream side. Time-dependent flow rates in the analysis of recession hydrographs could also be evaluated by this model.     

基于分数阶时间导数常Q黏弹本构关系的含黏滞流体双相VTI介质中波场数值模拟

分数阶微分算子具有描述历史依赖性和全域相关性的特质,本文利用这种特质描述双相介质固体骨架的黏弹性特征.基于Kjartansson常Q理论将含有分数阶时间导数的黏弹固体骨架各向异性本构关系与双相介质理论有机地结合起来,并引入流变学本构关系描述孔隙流体的黏滞性力学行为,提出一种新的基于分数阶时间导数常Q黏弹本构关系的含黏滞流体双相VTI模型.推导了相应的时间域波传播方程,然后对该方程进行了数值模拟.对整数阶导数采用高阶交错网格有限差分算法,对分数阶时间导数采用短时记忆中心差分算法,进行了不同相界、不同品质因子组及双层地质结构情况下该类介质中波场的数值模拟与特征分析.模拟结果表明：将含有分数阶时间导数的常Q黏弹固体骨架各向异性本构关系及孔隙流体的黏滞性本构关系引入双相介质理论是可行的,二者的结合能更好地反映地下介质的黏弹性特征,对于进一步认识波在黏弹各向异性孔隙介质中的传播机理具有重要意义,为反演和重构地下油气储层和结构奠定正演理论基础.     

A fuzzy‐based simulation method for modelling hydrological processes under uncertainty

In this study, a fuzzy‐based simulation method (FBSM) is developed for modelling hydrological processes associated with vague information through coupling fuzzy vertex analysis technique with distributed hydrological model. The FBSM can handle uncertainties existed as fuzzy sets in the hydrological modelling systems, and solutions under an associated number of α‐cut levels can be generated by solving 2ⁿ deterministic models. The lower reach of the Tarim River Basin in China is selected as a study case for demonstrating applicability of the proposed method. The developed model is calibrated and validated against observed groundwater elevation for four wells during the period 2000–2001, and generally performed acceptable for model Nash–Sutcliffe coefficient (R2) and correlation coefficient (R). The R2 is approximately over 0·65 and the correlation coefficient is higher than 0·90. Based on the technique of fuzzy simulation, the uncertainties of two parameters (K_H and LC) are reflected under different α‐cut levels. The results indicate that, under a lower degree of plausibility, the interval between the lower and upper bounds of the groundwater elevation is wider; conversely, a higher degree of plausibility would lead to a narrow interval. The main effect of K_H is more significant than the effect of LC at most well sites. The proposed method is useful for studying hydrological processes within a system containing multiple factors with uncertainties and providing support for identifying proper water resources management strategies. Copyright © 2010 John Wiley & Sons, Ltd.     

多目标优化下平原河网引调水改善水环境效果评估

引调水是改善平原河网地区水环境的重要方法之一,通过构建太湖流域走马塘东南片平原河网区一维水动力水质数学模型,研究不同引调水方案对区域水环境改善效果,确定引调水过程中的异质性因子.从决策目标、水质指标、空间指标3个层面综合考虑,构建环境效益与经济效益结合的多目标函数及评价体系,对引调水方案进行评估优选.结果表明:引调水流量较大时,能够在一定程度上改善区域水环境状况,规划方案下引调水5 d后,高锰酸盐指数、氨氮、总磷的平均改善率分别为30.7%、22.2%、26.4%;引调水时,区域河网中不同空间点位、不同水质指标之间的水质改善过程与效果都存在一定异质性;引调水水量、调度模式及污染源分布都会对调水后的河网区水质产生差异性影响;本研究建立的多目标评价体系较现有方法能够有效涵盖引调水中存在的异质性因子,从多个目标层面优选引调水方案,实现水量水质综合优化调控,为平原河网地区水环境长效管理与科学决策提供理论参考.     

A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty

In this study a simulation-based fuzzy chance-constrained programming (SFCCP) model is developed based on possibility theory. The model is solved through an indirect search approach which integrates fuzzy simulation, artificial neural network and simulated annealing techniques. This approach has the advantages of: (1) handling simulation and optimization problems under uncertainty associated with fuzzy parameters, (2) providing additional information (i.e. possibility of constraint satisfaction) indicating that how likely one can believe the decision results, (3) alleviating computational burdens in the optimization process, and (4) reducing the chances of being trapped in local optima. The model is applied to a petroleum-contaminated aquifer located in western Canada for supporting the optimal design of groundwater remediation systems. The model solutions provide optimal groundwater pumping rates for the 3, 5 and 10 years of pumping schemes. It is observed that the uncertainty significantly affects the remediation strategies. To mitigate such impacts, additional cost is required either for increased pumping rate or for reinforced site characterization.     

Agricultural effluent control under uncertainty: An inexact double-sided fuzzy chance-constrained model

An inexact double-sided fuzzy chance-constrained programming (IDFCCP) method was developed in this study and applied to an agricultural effluent control management problem. IDFCCP was formulated through incorporating interval linear programming (ILP) into a double-sided fuzzy chance-constrained programming (DFCCP) framework, and could be used to deal with uncertainties expressed as not only possibility distributions associated with both left- and right-hand-side components of constraints but also discrete intervals in the objective function. The study results indicated that IDFCCP allowed violation of system constraints at specified confidence levels, where each confidence level consisted of two reliability scenarios. This could lead to model solutions with high system benefits under acceptable risk magnitudes. Furthermore, the introduction of ILP allowed uncertain information presented as discrete intervals to be communicated into the optimization process, such that a variety of decision alternatives can be generated by adjusting the decision-variable values within their intervals. The proposed model could help decision makers establish various production patterns with cost-effective water quality management schemes under complex uncertainties, and gain in-depth insights into the trade-offs between system economy and reliability.