A two-phase grey fuzzy optimization approach for water quality management of a river system

An extension of the Grey Fuzzy Waste Load Allocation Model (GFWLAM) developed in an earlier work is presented here to address the problem of multiple solutions. Formulation of GFWLAM is based on the approach for solving fuzzy multiple objective optimization problems with max–min as the operator, which usually may not result in a unique solution. The multiple solutions of fuzzy multiobjective optimization model should be obtained as parametric equations or equations that represent a subspace. A two-phase optimization technique, two-phase GFWLAM, is developed to capture all alternative or multiple solutions of GFWLAM. The optimization model in Phase 1 is exactly same as the optimization model described in GFWLAM. The optimization model in Phase 2 maximizes the upper bounds of fractional removal levels of pollutants and minimizes the lower bounds of fractional removal levels of pollutants keeping the value of goal fulfillment level same as obtained from Phase 1. The widths of the interval-valued fractional removal levels play an important role in decision-making as these can be adjusted within their intervals by the decision-maker considering technical and economic feasibility in the final decision scheme. Two-phase GFWLAM widens the widths of interval-valued removal levels of pollutants, thus enhancing the flexibility in decision-making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.     

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.     

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.     

鄱阳湖退田还湖区避洪农业模式的探讨

以洪灾风险为主要风险确定了鄱阳湖退田还湖区内各种避洪农业模式的风险度量指标,并依此对各种可能模式的效益与风险作了分析.在此基础上,以收益最大化和风险最小化为目标函数,建立了退田还湖区综合避洪农业模式的多目标优化模型,采用加权法求出非劣解,并通过收益-风险的综合分析,为退田还湖区避洪农业发展的风险决策提供客观依据.     

Integrating water management,habitat modelling and water quality at the basin scale and environmental flow assessment: case study of the Tormes River,Spain

Abstract

Multidisciplinary models are useful for integrating different disciplines when addressing water planning and management problems. We combine water resources management, water quality and habitat analysis tools that were developed with the decision support system AQUATOOL at the basin scale. The water management model solves the allocation problem through network flow optimization and considers the environmental flows in some river stretches. Once volumes and flows are estimated, the water quality model is applied. Furthermore, the flows are evaluated from an ecological perspective using time series of aquatic species habitat indicators. This approach was applied in the Tormes River Water System, where agricultural demands jeopardize the environmental needs of the river ecosystem. Additionally, water quality problems in the lower part of the river result from wastewater loading and agricultural pollution. Our methodological framework can be used to define water management rules that maintain water supply, aquatic ecosystem and legal standards of water quality. The integration of ecological and water management criteria in a software platform with objective criteria and heuristic optimization procedures allows realistic assessment and application of environmental flows to be made. Here, we improve the general methodological framework by assessing the hydrological alteration of selected environmental flow regime scenarios.

Editor D. Koutsoyiannis; Guest editor M. Acreman

Citation Paredes-Arquiola, J., Solera, A., Martinez-Capel, F., Momblanch, A., and Andreu, J., 2014. Integrating water management, habitat modelling and water quality at the basin scale and environmental flow assessment: case study of the Tormes River, Spain. Hydrological Sciences Journal, 59 (3–4), 878–889.     

Sensitivity,responsivity, stability and irreversibility as multiple objectives in civil systems

This paper recommends the consideration of sensitivity, stability, risk, and irreversibility as objective functions in water resource management models within the framework of multiobjective analysis. Six major sources of uncertainties and errors in systems modeling are identified. They are associated with the following model characteristics: model structure (topology), model parameters, model scope or focus, data, optimization technique, and human subjectivity. In particular, the major objective of this paper is to set the stage for the development of an analytical and operational multiobjective framework which will provide decision-makers and plamers with alternatives that consider systems' sensitivity, responsivity, stability and irreversibility along with cost and other performance indices as multiple objectives. This type of a framework should have a very wide spectrum of applications in water and related land resources, environmental studies, energy, and others. The Surrogate Worth Trade-off method is proposed for the solution of the resulting multiobjective optimization problem.     

Using fuzzy operators to address the complexity in decision making of water resources redistribution in two neighboring river basins

This paper emphasizes the use of fuzzy sets for incorporating objective and subjective uncertainties to address coevolutionary alignment of a suite of water resources redistribution alternatives in a transboundary channel–reservoir system. The highlighted decision making complexity arises from the interactions between two neighboring water systems (i.e., the Tseng-Wen and Kao-Ping River Basins, South Taiwan) where a pending diversion plan has been under intensive debate for over a decade. While the local stakeholders make uncertain science linked with uncertain politics resulting in endless delay of the diversion plan, the environmental advocacy groups stress the increasing concern of loss of biological integrity due to changes of land use when sharing water resources across the boundary. Consequently, there is a need to generate a novel integration that enables us to consider a vast number of internal weirs, water intakes, reservoirs, drainage ditches, and transfer pipelines within the basin and bring out the connectivity via diversion between these two neighboring river basins under uncertainty. To explore the managerial implications with varying risk perception and risk attitude, four types of fuzzy operators tailored for the fuzzy multi-objective decision analysis depict greater flexibility in representing the complexity of possible trade-offs among those alternatives. These trade-offs in the multi-objective evaluation context are constrained by physical, chemical, socioeconomic, managerial, and technical factors reflecting the needs for adaptive water resources management. Findings indicates that the use of fuzzy operators is instructive, which could provide unique guidance for enlightening the potential barriers in sustainable water resources management at the regional scale.     

Optimization of the industrial structure facing sustainable development in resource-based city subjected to water resources under uncertainty

An inexact stochastic fuzzy programming (ISFP) approach has been developed for the optimization of the industrial structure in resource-based city subjected to water resources under uncertainty in present study. The ISFP method incorporates the techniques of inexact stochastic programming and inexact fuzzy chance-constrained programming, where the uncertainties are expressed as interval, fuzzy sets, and probability distribution, respectively. Moreover, it can also examine the risk of violating fuzzy tolerance constraints. The developed method is subsequently employed in a realistic case for industrial development in the Jinchang city, Gansu province, China. The result can help to analyze whether the water resources carrying capacity of Jinchang can meet the need of local economic development plan under uncertainty and help decision maker to optimize the industry structure under water resource constraints to meet the maximum economic efficiency.     

A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems

Groundwater characterization involves the resolution of unknown system characteristics from observation data, and is often classified as an inverse problem. Inverse problems are difficult to solve due to natural ill-posedness and computational intractability. Here we adopt the use of a simulation–optimization approach that couples a numerical pollutant-transport simulation model with evolutionary search algorithms for solution of the inverse problem. In this approach, the numerical transport model is solved iteratively during the evolutionary search. This process can be computationally intensive since several hundreds to thousands of forward model evaluations are typically required for solution. Given the potential computational intractability of such a simulation–optimization approach, parallel computation is employed to ease and enable the solution of such problems. In this paper, several variations of a groundwater source identification problem is examined in terms of solution quality and computational performance. The computational experiments were performed on the TeraGrid cluster available at the National Center for Supercomputing Applications. The results demonstrate the performance of the parallel simulation–optimization approach in terms of solution quality and computational performance.     

River water quality response under hypothetical climate change scenarios in Tunga‐Bhadra river,India

Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low‐flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga‐Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright © 2011 John Wiley & Sons, Ltd.     

The application of the land surface model for calculating river runoff in high latitudes

The potentialities of the land surface model as applied to the calculation of river runoff in high latitudes were examined. Three approaches were used to specify input data based on meteorological data and land surface parameters. A method was developed for automated optimization of some model parameters by using direct search of minimum of root-mean-square deviation between the calculated and measured streamflow values. The global data sets are shown to be applicable in principle for hydrological calculations.     

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.     

Water quality model of the Danube in its Bulgarian part

Models for predicting river water quality could assist protection and utilization of water resources under alternative management strategies. The model of the water quality characteristics of the Danube is based on an ecosystem approach. Considered are all waste water discharges and inflow of tributaries among the catchment area. The identification of model parameters is realized according to the method of integral transformation. For the performance of the model a computer program was elaborated. It is applied in predicting the water quality of the Danube considering almost 20 indicators for the abiotic and biotic parameters of river ecosystem.     

Development of an inexact two-stage stochastic model with downside risk control for water quality management and decision analysis under uncertainty

Water quality management along rivers involves making water-allocation plans, establishing water quality goals, and controlling pollutant discharges, which is complicated itself but further challenged by existence of uncertainties. In this study, an inexact two-stage stochastic downside risk-aversion programming (ITSDP) model is developed for supporting regional water resources allocation and water quality management problems under uncertainties. The ITSDP method is a hybrid of interval-parameter programming, two-stage stochastic programming, and downside risk measure to tackle uncertainties described in terms of interval values and probability distributions. A water quality simulation model was provided for reflecting the relationship between the water resources allocation, wastewater discharge, and environmental responses. The proposed approach was applied to a hypothetical case for a shared stream water quality management with one municipal, three industrial and two agricultural sectors. A number of scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk-aversion into optimization process, and generate a trade-off between the system economy and stability. They could be helpful for seeking cost-effective management strategies under uncertainties, and gaining an in-depth insight into the water quality management system characteristics, and make cost-effective decisions.     

Non-linear fuzzy-set based uncertainty propagation for improved DO prediction using multiple-linear regression

In this paper, a new non-linear fuzzy-set based methodology is proposed to characterize and propagate uncertainty through a multiple linear regression (MLR) model to predict DO using flow and water temperature as the regressors. The output is depicted as probabilistic rather than deterministic and is used to calculate the risk of low DO concentration. To demonstrate the new method, data from the Bow River in Calgary, Alberta from 2006 to 2008 are used. Low DO concentration has been occasionally observed in the river and correctly predicting, and quantifying the associated uncertainty and variability of DO is of interest to the City of Calgary. Flow, temperature and DO data were used to construct five MLR models, using different combinations of linear and non-linear fuzzy membership functions. The results show that non-linear representation of variance is superior to the linear approach based on model performance. Normal and Gumbel based membership functions produced the best results. The outputs from two non-linear fuzzy membership models were used to calculate risk of low DO. The predicted risk was between 3.9 and 4.9 %. This is an improvement over the traditional method, which can not indicate a risk of low DO for the same time period. This study demonstrates that water resource managers can adequately use MLR models to predict the risk of low DO using abiotic factors.     

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.     

A fuzzy-Markov-chain-based analysis method for reservoir operation

In this study, a fuzzy-Markov-chain-based stochastic dynamic programming (FM-SDP) method is developed for tackling uncertainties expressed as fuzzy sets and distributions with fuzzy probability (DFPs) in reservoir operation. The concept of DFPs used in Markov chain is presented as an extended form for expressing uncertainties including both stochastic and fuzzy characteristics. A fuzzy dominance index analysis approach is proposed for solving multiple fuzzy sets and DPFs in the proposed FM-SDP model. Solutions under a set of α-cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels. The developed method is applied to a case study of a reservoir operation system. Solutions from FM-SDP provide a range of desired water-release policies under various system conditions for reservoir operation decision makers, reflecting dynamic and dual uncertain features of water availability simultaneously. The results indicate that the FM-SDP method could be applicable to practical problems for decision makers to obtain insight regarding the tradeoffs between economic and system reliability criteria. Willingness to obtain a lower benefit may guarantee meeting system-constraint demands; conversely, a desire to acquire a higher benefit could run into a higher risk of violating system constraints.     

River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model

Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.     

Multiobjective optimal FLC driven hybrid mass damper system for torsionally coupled,seismically excited structures

In most of the research work on structural vibration control only two‐dimensional plane structural modelling has been considered, although only a few practical building structures can be modelled as planar structures. Therefore, these methods are not directly applicable to the majority of the practical building structures. This paper discusses the design of a multiobjective optimal fuzzy logic controller (FLC) driven hybrid mass damper (HMD) system for seismically excited torsionally coupled building structures. Floor acceleration and velocity information have been used as feedback to the fuzzy logic controller. A three branch tournament Genetic Algorithm has been used for the multiobjective optimal design of the FLC driven HMD system, where the minimization of the non‐dimensionalized peak displacement, acceleration and rotation of the structure about its vertical axis, have been as the three objective functions. The proposed multiobjective optimal fuzzy logic controller has been verified for an example problem reported in the literature. This HMD system consists of four HMDs arranged in such a way that the system can control the torsional mode of vibration effectively in addition to the flexure modes of vibration. Copyright © 2002 John Wiley & Sons, Ltd.     

RAPID applied to the SIM‐France model

SIM‐France is a large connected atmosphere/land surface/river/groundwater modelling system that simulates the water cycle throughout metropolitan France. The work presented in this study investigates the replacement of the river routing scheme in SIM‐France by a river network model called RAPID to enhance the capacity to relate simulated flows to river gauges and to take advantage of the automated parameter estimation procedure of RAPID. RAPID was run with SIM‐France over a 10‐year period and results compared with those of the previous river routing scheme. We found that while the formulation of RAPID enhanced the functionality of SIM‐France, the flow simulations are comparable in accuracy to those previously obtained by SIM‐France. Sub‐basin optimization of RAPID parameters was found to increase model efficiency. A single criterion for quantifying the quality of river flow simulations using several river gauges globally in a river network is developed that normalizes the square error of modelled flow to allow equal treatment of all gauging stations regardless of the magnitude of flow. The use of this criterion as the cost function for parameter estimation in RAPID allows better results than by increasing the degree of spatial variability in optimization of model parameters. Likewise, increased spatial variability of RAPID parameters through accounting for topography is shown to enhance model performance. Copyright © 2011 John Wiley & Sons, Ltd.