A New Integrated MADM Technique Combined with ANP,FTOPSIS and Fuzzy Max-Min Set Method for Evaluating Water Transfer Projects

In sustainable water resources management, it is essential to rank inter-basin water transfer projects. This task is difficult due to many different conflict criteria, complex relations among criteria and various judgments of decision makers. In this paper, an integrated multiple attribute group decision making method consists of ANP (Analytical Network Process), fuzzy TOPSIS and fuzzy max-min set methods is proposed for evaluating water transfer projects. A set of over 60 criteria in social, environmental and economic sectors are used for ranking four water transfer projects in Karun River based on three decision maker judgments. A key novelty of the proposed methodology is its ability to model both complex relations among different criteria in water management and the influence of decision maker judgments’ weights on the final ranking in group decision making problem. The procedure starts by obtaining the priority of water transfer projects and the weight of each decision maker judgments by employing ANP and fuzzy TOPSIS, respectively. These weights are used as inputs in the fuzzy max-min set method. Then the effects of decision maker weights on the final ranking are determined in fuzzy environment. Finally, the sensitivity analysis of decision makers’ weights has been conducted. The results show that the proposed method is an effective tool for group decision making problems by considering different criteria and decision makers’ weights.     

An Intelligent Decision Support System for Management of Floods

Integrating human knowledge with modeling tools, an intelligent decision support system (DSS) is developed to assist decision makers during different phases of flood management. The DSS is developed as a virtual planning tool and can address both engineering and non-engineering issues related to flood management. Different models (hydrodynamic, forecasting, and economic) that are part of the DSS share data and communicate with each other by providing feedback. The DSS is able to assist in: selecting suitable flood damage reduction options (using an expert system approach); forecasting floods (using artificial neural networks approach); modeling the operation of flood control structures; and describing the impacts (area flooded and damage) of floods in time and space. The proposed DSS is implemented for the Red River Basin in Manitoba, Canada. The results from the test application of DSS for 1997 flood in the Red River Basin are very promising. The DSS is able to predict the peak flows with 2% error and reveals that with revised operating rules the contribution of Assiniboine River to the flooding of Winnipeg city can be significantly reduced. The decision support environment allows a number of “what-if” type questions to be asked and answered, thus, multiple decisions can be tried without having to deal with the real life consequences.     

A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems

The complexity of water resources management increases when decisions about environmental and social issues are considered in addition to economic efficiency. Such complexities are further compounded by multiple uncertainties about the consequences of potential management decisions. In this paper, a new fuzzy-stochastic multiple criteria decision-making approach is proposed for water resources management in which a variety of criteria in terms of economic, environmental and social dimensions are identified and taken into account. The goal is to evaluate multiple conflicting criteria under uncertainties and to rank a set of management alternatives. The methodology uses a simulation-optimization water management model of a strongly interacting groundwater-agriculture system to enumerate criteria based on these bio-physical process interactions. Fuzzy and/or qualitative information regarding the decision-making process for which quantitative data is not available are evaluated in linguistic terms. Afterwards, Monte Carlo simulation is applied to combine these information and to generate a probabilistic decision matrix of management alternatives versus criteria in an uncertain environment. Based on this outcome, total performance values of the management alternatives are calculated using ordered weighted averaging. The proposed approach is applied to a real world example, where excessive groundwater withdrawal from the coastal aquifer for irrigated agriculture has resulted in saltwater intrusion, threatening the economical basis of farmers and associated societal impacts. The analysis has provided potential decision alternatives which can serve as a platform for negotiation and further exploration. Furthermore, sensitivity of different inputs to resulting rankings is investigated. It is found that decision makers’ risk aversion and risk taking attitude may yield different rankings. The presented approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management.     

FUZZY OPTIMAL DECISIONS FOR MULTIOBJECTIVE FLOOD CONTROL SYSTEM OF CASCADE RESERVOIRS

１．　ＩＮＴＲＯＤＵＣＴＩＯＮＴｈｅｏｐｅｒａｔｉｏｎｄｅｃｉｓｉｏｎｓｏｆｍｕｌｔｉｏｂｊｅｃｔｉｖｅｆｌｏｏｄｃｏｎｔｒｏｌｓｙｓｔｅｍｓａｒｅｎｏｔｏｎｌｙｒｅｌａｔｅｄｔｏｔｈｅｓａｆｅｔｙｏｆｗｈｏｌｅｓｙｓｔｅｍａｎｄｔｈｅｆｌｏｏｄｄａｍａｇｅｌｏｓｓ，ｂｕｔａｌｓｏｈａｖｅｇｒｅａｔｉｎｆｌｕｅｎｃｅｏｎｔｈｅｉｒｃｏｍｐｒｅｈｅｎｓｉｖｅｂｅｎｅｆｉｔ．Ｉｔｉｓｖｅｒｙｉｍｐｏｒｔａｎｔｔｏｓｔｕｄｙｔｈｅｐｒａｃｔｉｃａｌａｎｄｅｆｆｅｃｔｉｖｅｍｅｔｈｏｄｓｏｆｆｌｏｏｄ…     

Flood Control Operations Based on the Theory of Variable Fuzzy Sets

Flood control decisions are often involved with quantitative and qualitative criteria. In this paper, a decision model is presented for flood control operations based on the theory of variable fuzzy sets. Using dual comparison, two models computing relative membership grades with qualitative and quantitative criteria are established, respectively. A method integrating subjective preference and iterative weights is proposed for weight-assessment. First, an initial solution of criteria weights is obtained by using proposed fuzzy optimal iteration model. Then, according to their knowledge related to real time flood operations, operators may modify the initial weights if necessary. When the relative membership grades of alternatives belonging to all rankings are fixed by using multi-criterion variable fuzzy model proposed, the decision alternative is chosen according to the ranking characteristic value computed using a defuzzification equation. The case study of Fengman Reservoir flood operation (in China) is provided to illustrate the application of the proposed method. With the incorporation of operator’s knowledge related to flood operations, the proposed model is flexible and practical.     

梯级水库群防洪系统的多目标洪水调度决策的模糊优选

本文将传统的优化技术与新发展起来的模糊集合理论有机地结合起来，针对洪水实际调度的特点，提出了一个切实可行的梯级水库群洪水调度决策的模糊优选模型，其特点是调度方案作为决策，水库的泄流过程作为状态，然后用模糊优选技术选择系统的洪水调度方案，最后，结合丰满－白山梯级水库的洪水联调实例进行了说明。     

A new approach to aid urban water management decision making using trade-off sacrifice modelled by fuzzy logic.

An approach to aid decision making for urban water management is presented that is based on the concept of trade-off sacrifice level in pairwise comparisons between criteria, modelled using fuzzy logic. This approach is illustrated by a case study - selection of alternative water supplies for a Sydney household. Four key decision making criteria covering health, economic, environment and technical aspects are selected: annual probability of infection, life cycle energy use, life cycle cost and reliability. The decision making problem is to select between cases with different volume and application of recycled greywater and rainwater in light of the four criteria. Decision maker's preference is expressed by five levels of trade-off sacrifice between pairs of criteria. The decision makers can assign their preferences for sacrifice level by linguistic assessment and the output trade-off weight (TOW). Measures of decision makers' perceived trade-off level are modelled by a rule-based fuzzy logic control system. The final analysis shows the performance for each sacrifice class for each case, to aid overall decision making with stakeholders.     

信息融合技术在防洪决策中的应用分析

流域防洪调度决策是一个庞大的复杂巨系统问题,需要利用信息融合技术对具有不确定性、模糊性的多信息源、多媒体、多格式的防洪信息进行综合处理,以便为分析防洪领域的模型、专家、决策人员提供及时、可靠、高效的信息。现以信息融合技术为基础,分析了信息融合技术特点及其在防洪决策领域中的应用需求,并提出了防洪信息的融合结构,包括防洪信息融合的层次、拓扑和体系结构等;归纳指出了信息融合技术在防洪决策中应用的关键环节,即融合系统、数据管理和融合算法,并对其应用研究情况进行了论述分析;最后指出了信息融合技术在防洪决策中应用的发展趋势。     

Robust Water Supply Chain Network Design under Uncertainty in Capacity

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

     

大连市水资源利用与宏观经济协调发展规划多目标群决策模型与方法

以大连市水资源开发利用与宏观经济协调可持续发展为研究背景，建立了大连市宏观经济水资源发展规划多目标群决策模型。根据该决策模型具有的多层次多目标多决策者的特点，结合陈守煜建立的工程模糊集理论与切比雪夫决策方法，提出了模糊切比雪夫多目标群决策方法。结果表明，该决策模型与模糊切比雪夫多目标群决策方法是有效和可行的。     

Dual-Interval Two-Stage Optimization for Flood Management and Risk Analyses

In this study, a dual interval two-stage restricted-recourse programming (DITRP) method is developed for flood-diversion planning under uncertainty. Compared with other conventional methods, DITRP improves upon them by addressing system uncertainties with complex presentations and incorporating subjective information within its optimization framework. Uncertainties in DITRP can be represented as probability distributions and intervals. In addition, the dual-interval concept is presented when the available information is highly uncertain for boundaries of intervals. Moreover, decision makers’ attitudes towards system risk can be reflected using a restricted-resource measure by controlling the variability of the recourse cost. The method has been applied to a case study of flood management. The results indicate that reasonable solutions for planning flood management practice have been generated which are related to decisions of flood-diversion. Several policy scenarios are analyzed, assisting in gaining insight into the tradeoffs between risk and cost.     

动态拦蓄洪尾风险调度模型初探

分析了实施水库拦蓄洪尾风险调度的可行性、必要性和影响拦蓄水位确定的主要因素；提出了动态拦蓄洪尾风险调度模型，该模型可以表达汛期不同时间水库拦蓄洪尾水位的动态变化；从目标函数的物理意义出发，提出了模糊综合评判法和试算法相结合的求解方法，该方法能最大限度体现决策者的偏好，保障解的合理性和可操作性。实例研究表明，文中提出的方法是合理、有效的。     

Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty

In this study, an interval-parameter two-stage stochastic semi-infinite programming (ITSSP) method was developed for water resources management under uncertainty. As a new extension of mathematical programming methods, the developed ITSSP approach has advantages in uncertainty reflection and policy analysis. In order to better account for uncertainties, the ITSSP approach is expressed with discrete intervals, functional intervals and probability density functions. The ITSSP method integrates the two-stage stochastic programming (TSP), interval programming (IP) and semi-infinite programming (SIP) within a general optimization framework. The ITSSP has an infinite number of constraints because it uses functional intervals with time (t) being an independent variable. The different t values within the range [0, 90] lead to different constraints. At same time, ITSSP also includes probability distribution information. The ITSSP method can incorporate pre-defined water resource management policies directly into its optimization process to analyze various policy scenarios having different economic penalties when the promised amounts are not delivered. The model is applied to a water resource management system with three users and four periods (corresponding to winter, spring, summer and fall, respectively). Solutions of the ITSSP model provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable interval solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flow. The obtained solutions are useful for decision makers to obtain insight regarding the tradeoffs between environmental, economic and system reliability criteria.     

A Simulation-Based Optimization Model for Flood Management on a Watershed Scale

Using structural and nonstructural measures for flood damage reduction is a long-standing problem in water resources planning and management. In the present study, an algorithm is presented for the optimal design of structural and nonstructural flood mitigation measures based on simulation-based optimization approach. For this purpose, the MIKE-11 simulation model, a one-dimensional hydrodynamic model, was used to calculate the potential damages of different flood scenarios under the various combinations of structural and nonstructural measures and this model was coupled with the NSGA-II multi-objective optimization model to provide the optimal Pareto solutions between two conflict objectives of minimizing the investment costs of flood mitigation measures and the potential damages of the floodplain. The proposed model was then applied to a small watershed in central part of Iran as a case study and the optimal trade-off solutions were calculated for different flood scenarios. Using these trade-offs, for each level of funding, decision makers can assign the optimal design of flood mitigation measures considering decision criteria.     

An Inexact Two-Stage Water Resources Allocation Model for Sustainable Development and Management Under Uncertainty

In this study, an inexact two-stage water resources allocation (ITWR) model is put forward for supporting sustainable development and management of water resources in Sanjiang Plain, China, which is in such a situation, with multi-water source, multi-water supply subarea, multi-water user and multi-planning goal. The costs of net system, water supply and recourse are analyzed. The developed ITWR model, which shows a strong ability in tacking with various uncertain factors in probability distributions and discrete interval numbers, mixes the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. And it also has formed an effective link in such a conflict between the policy scenarios and the associated various levels of economic penalties, when the pre-allocation targets of water resources are violated. Based on this model, a series of scenarios under different levels of pre-allocation water is done and different degrees of water surplus and shortage are obtained correspondingly. The results indicate that the reasonable distribution plans with maximum system benefit and minimum system-failure risk have been generated. And these results are valuable for saving water resources to realize its sustainable development and mitigating the penalty to gain economic benefits maximum, and thus some desired results are provided for decision makers in tackling with a complex and uncertain water-resource system.     

Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility

Identifying flood prone areas is essential for basin management. In this paper, a spatial prediction technology of flood susceptibility based on multiple kernel learning (MKL) is proposed. We establish the flood susceptibility model by using EasyMKL, nonlinear MKL (NLMKL), Representative MKL(RMKL), Generalized MKL(GMKL), support vector machine(SVM) with linear kernel and SVM with Gaussian radial base function(RBF) kernel, The spatial prediction of flood susceptibility in Sanhuajian basin of the Yellow River is carried out. We use MODIS remote sensing images to obtain historical flood inundation sites in the study area. Then, ten flood conditioning factors are used as inputs to the flood susceptibility model. The model performance is evaluated in terms of accuracy (ACC), balanced F Score (F1 score), and areas under the curve (AUC). According to the results, MKL significantly outperforms the SVM adopting single kernel, and NLMKL(ACC=0.833,F1=0.841,AUC=0.889) demonstrates the best comprehensive performance. The flood susceptibility map generated by MODIS remote sensing images and MKL, therefore, can provide effective help for researchers and decision makers in flood management.

     

IFTSQP: An Inexact Optimization Model for Water Resources Management under Uncertainty

Abstract

An interval-fuzzy two-stage quadratic programming (IFTSQP) method is developed for water resources management under uncertainty. The methodincorporates techniques of interval-parameter programming, two-stage stochastic programming, and fuzzy quadratic programming within a general optimization framework to tackle multiple uncertainties presented as intervals, fuzzy sets and probability distributions. In the model formulation, multiple control variables are adopted to handle independent uncertainties in the model's right-hand sides; fuzzy quadratic terms are used in the objective function to minimize the variation in satisfaction degrees among the constraints. Moreover, the method can support the analysis of policy scenarios that are associated with economic penalties when the promised targets are violated. The developed method is then applied to a case study of water resources management planning. The results indicate that reasonable solutions have been obtained. They can help provide bases for identifying desired water-allocation plans with a maximized system benefit and a minimized constraint-violation risk.     

基于BP神经网络的贝叶斯概率水文预报模型

本文在贝叶斯概率水文预报系统(BFS)框架之上，研究了双牌水库水文预报的不确定性，建立了流量先验分布及似然函数的BP神经网络模型，并通过Markov链Monte Carlo(MCMC)方法求解得到流量后验分布及其统计参数。通过对双牌水库历史洪水的研究结果表明，基于BP神经网络的BFS不仅显著提高了预报精度，而且为防洪决策提供了更多的信息，使得预报人员在决策中能考虑预报的不确定性，定量的估计各种决策的风险和后果。     

A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region,Hunan, Central China

Floods, the most common natural hazard in the world, cause serious loss in terms of lives, buildings, and infrastructures. As a consequence, the need for flood risk assessment has become critical. Using a semi-quantitative model and fuzzy analytic hierarchy process (FAHP) weighting approach, this paper assessed flood risk in the Dongting Lake region, Hunan Province, Central China, an area where flood hazards frequently occur. The model was designed using spatial multi-criteria analysis (SMCA) techniques in a Geographic Information System (GIS). A GIS database of indicators for the evaluation of hazard and vulnerability was created. Each indicator was analyzed, standardized, and weighted; after which, the weights of the indicators were combined to obtain the final flood risk index map. Using the flood risk index, the study area was classified into five categories of flood risk: very low, low, medium, high, and very high. The high and very high risk zones are mainly concentrated in the northern and central plains. The results obtained can provide useful information for decision makers and insurance companies.     

Flood Forecasting and Decision Making in the new Millennium. Where are We?

This paper reviews the development of real time flood forecasting systems from the early 1970 approaches to the recent probabilistic ones. A preliminary discussion on the motivations for developing real time flood forecasting systems is introduced to explain their evolution in the last four to five decades. It will be shown how recent probabilistic flood forecasts are more robust and effective than the traditional deterministic ones. In particular, when combined with Bayesian decision approaches, probabilistic forecasts are the most appropriate tools for rational decision making in flood warning and flood management.Moreover, they allow taking into account the information from several models to be taken into account by combining into a unique predictive density the deterministic predictions of several hydrological or hydraulic models of a different nature, while in the multi-temporal forecasting extensions, they provide to answers questions such as: Which is the probability of overtopping a dyke in the next 24 h? When will this event be more likely to occur during the next 24 h?The work concludes with a discussion on the still unresolved problems, namely how decisions makers can fully take advantage of the probabilistic forecasts and how these forecasts must be communicated to them in order to meet this objective.