An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management

Water quality management is complicated with a variety of uncertainties and nonlinearities. This leads to difficulties in formulating and solving the resulting inexact nonlinear optimization problems. In this study, an inexact chance-constrained quadratic programming (ICCQP) model was developed for stream water quality management. A multi-segment stream water quality (MSWQ) simulation model was provided for establishing the relationship between environmental responses and pollution-control actions. The relationship was described by transformation matrices and vectors that could be used directly in a multi-point-source waste reduction (MWR) optimization model as water-quality constraints. The interval quadratic polynomials were employed to reflect the nonlinearities and uncertainties associated with wastewater treatment costs. Uncertainties associated with the water-quality parameters were projected into the transformation matrices and vectors through Monte Carlo simulation. Uncertainties derived from water quality standards were characterized as random variables with normal probability distributions. The proposed ICCQP model was applied to a water quality management problem in the Changsha section of the Xiangjiang River in China. The results demonstrated that the proposed optimization model could effectively communicate uncertainties into the optimization process, and generate inexact solutions containing a spectrum of wastewater treatment options. Decision alternatives could then be obtained by adjusting different combinations of the decision variables within their solution intervals. Solutions from the ICCQP model could be used to analyze tradeoffs between the wastewater treatment cost and system-failure risk due to inherent uncertainties. The results are valuable for supporting decision makers in seeking cost-effective water management strategies.     

A Compromise Programming Model to Integrated Urban Water Management

Integrated urban water management is an important and critical matter in every city and country. Many objectives and criteria such as satisfaction of the urban water consumers, the national benefits and social hazards must be considered in the integrated urban water management. So the integrated urban water management can be considered as a multi-objective problem. In this paper, a mathematical model which uses the compromise programming model is presented to optimize this multi-objective problem. Three famous objectives involving water distribution cost, leakage water and social satisfaction level are considered. To evaluate the performance and efficiency of the proposed model, Hamedan potable water network is chosen as a case study. Results show that the proposed model is capable to present effective solutions for the considered problem. So the proposed mathematical model can be used as an efficient tool for the integrated urban water management in every urban area.     

Interval-parameter Two-stage Stochastic Nonlinear Programming for Water Resources Management under Uncertainty

Over the past decades, controversial and conflict-laden water allocation issues among competing interests have raised increasing concerns. In this research, an interval-parameter two-stage stochastic nonlinear programming (ITNP) method is developed for supporting decisions of water-resources allocation within a multi-reservoir system. The ITNP can handle uncertainties expressed as both probability distributions and discrete intervals. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised allocation targets are violated. Moreover, it can deal with nonlinearities in the objective function such that the economies-of-scale effects in the stochastic program can be quantified. The proposed method is applied to a case study of water-resources allocation within a multi-user, multi-region and multi-reservoir context for demonstrating its applicability. The results indicate that reasonable solutions have been generated, which present as combined interval and distributional information. They provide desired water allocation plans with a maximized economic benefit and a minimized system-disruption risk. The results also demonstrate that a proper policy for water allocation can help not only mitigate the penalty due to insufficient supply but also reduce the waste of water resources.     

An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management

An inexact two-stage fuzzy-stochastic programming (ITFSP) method is developed for water resources management under uncertainty. Fuzzy sets theory is introduced to represent various punishment policies under different water availability conditions. As an extension of conventional two-stage stochastic programming (TSP) method, two special characteristics of the proposed approach make it unique compared with existing approaches. One is it could handle flexible penalty rates, which are much reasonable for both of the authorities and users, and have seldom been considered in the TSP framework. The other is uncertain information expressed as discrete intervals and probability distribution functions can be effectively reflected in the optimization processes and solutions. After formulating the model, a hypothetical case is employed for demonstrating its applicability under two scenarios, where the inflow is divided into four and eight intervals, respectively. The results indicate that reasonable solutions have been obtained. They provide desired allocation patterns with maximized system benefit under two feasibility levels. The solutions present as stable intervals with different risk levels in violating the water demands, and can be used for generating decision alternatives. Comparisons of the solution from the ITFSP with that from the ITSP (inexact two-stage stochastic programming) and TSP approach are also undertaken. It shows that the ITFSP could produce more system benefit than existing methods and deal with flexible penalty policies for better water management and utilization.     

淮河流域水质管理模型

淮河流域水质管理模型作为分析研究淮河流域水污染规律的工具 ,以淮河入洪泽湖河口以上流域为研究区域。在深入分析研究区域内水文气象、自然地理、河道和水流特性以及水污染特点的基础上 ,利用Mike 11,MIKEBASIN和ArcView软件研制和开发水质管理模型 ,为有效实施水污染防治和水资源管理提供依据。简要介绍模型的结构 ,主要参数的确定 ,模型的结果和模型的应用情况     

区域水资源模糊多目标规划模型及求解

针对区域水资源系统规划的多目标性和各目标的不精确性,建立了区域水资源系统模糊多目标线性规划模型。该模型改变了以往按确定性目标进行优化的思路,只需要给定目标值一个范围,就可以在规定的目标范围内进行优化求解。在求解过程中,建立了以各模糊目标隶属度值最大化为目标的模糊目标规划模型,采用目标隶属函数正负偏差量变量替换的方法,用线性目标规划的方法求解。经过实例验证,该模型求解比较简单,求解结果合理,与普通规划结果相比水资源模糊目标规划在规定目标的范围内取得了更好的优化结果。     

水资源模糊定价模型

阐述水资源价值系统的复杂性和模糊性，分析与水资源价值评价有关的水量，水质及社会经济要素，探讨水资源价值模糊综合评价数学模型和水资源价格计算模型，并从水费承受指数的概念出发，分析确定水资源价格向量，将水资源价格与水的用途和效益联系起来，使水资源价格更趋合理。     

Two-Stage Interval-Parameter Stochastic Programming Model Based on Adaptive Water Resource Management

Water resource planning is often associated with system complexities and uncertainties, such as issues of precipitation randomness and complex the complexity of human social activities. In this study, a two-stage interval-parameter stochastic programming (TISP) model in conjunction with an adaptive water resource management (AWRM) model was applied. Compared to other optimization models, AWRM can address interactions between different water users and account for regional water exchange processes, and TISP models overcome the uncertainties of a water resource system by introducing interval-parameter and probability distribution methods. Reasonable solutions obtained by applying these models to a multi-water-resource, multi-region case show that in AWRM models, water can flow from a region of low efficiency to a region of high efficiency, improving water use efficiency. Under conditions of extreme scarcity, water can flow in the opposite direction thus ensuring regional minimum water requirements, enhancing system stability and reducing the probability of system paralysis. In policy making, optimistic water policies correspond to higher incomes but may be subject to higher risks of system failure. Alternatively, conservative policies are associated with a lower risk of system failure but easily waste water resources.     

A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management

Flood, as a serious worldwide environment problem, can lead to detrimental economic losses and fatalities. Effective flood control is desired to mitigate the adverse impacts of flooding and the associated flood risk through development of cost-effective and efficient flood management decisions and policies. A bi-level fuzzy two-stage stochastic programming model, named BIFS model is developed in this study to provide decision support for economic analysis of flood management. The BIFS model is capable of not only addressing the sequential decision making issue involving the two-level decision makers, but also correcting the pre-regulated flood management decisions before the occurrence of a flood event in the two-stage environment. The probabilistic and non-probabilistic uncertainties expressed as probability density functions and fuzzy sets are quantitatively analyzed. The overall satisfaction solution is obtained for meeting the goals of the two-level decision makers by compromising, reflecting the tradeoffs among various decision makers in the two decision-making levels. The results of application of the BIFS model to a representative case study indicate informed decision strategies for flood management. Tradeoffs between economic objectives and uncertainty-averse attitudes of decision makers are quantified.     

基于数据库和GIS的水质模糊综合评价模型

根据地下水污染调查评价的需要,设计并开发了基于地下水污染调查数据库和GIS的水质模糊综合评价模型,该模型将模糊数学理论和数据库管理技术与GIS技术结合,实现了数据录入、管理、评价、制图的一体化流程.通过在实际项目中应用,普遍反映评价界面友好、操作简单、易用,结果符合实际情况,适合区域地下水水质的综合评价,具有较好的推广应用价值.     

A New Interval Two-stage Stochastic Programming with CVaR for Water Resources Management

Water Resources Management - In area of water resources management, decision-makers usually need to make plans under various uncertainties in order to achieve the maximal total net benefits and...     

A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir

Accurate prediction and monitoring of water level in reservoirs is an important task for the planning, designing, and construction of river-shore structures, and in taking decisions regarding irrigation management and domestic water supply. In this work, a novel probabilistic nonlinear approach based on a hybrid Bayesian network model with exponential residual correction has been proposed for prediction of reservoir water level on daily basis. The proposed approach has been implemented for forecasting daily water levels of Mayurakshi reservoir (Jharkhand, India), using a historic data set of 22 years. A comparative study has also been carried out with linear model (ARIMA) and nonlinear approaches (ANN, standard Bayesian network (BN)) in terms of various performance measures. The proposed approach is comparable with the observed values on every aspect of prediction, and can be applied in case of scarce data, particularly when forcing parameters such as precipitation and other meteorological data are not available.     

模糊综合评价法在长江下游贵池河段水质评价中的应用

对水质评价存在诸多方法,而模糊综合评价法具有评价结果合理、更接近客观实际等优点,因而得到越来越广泛的应用。通过模糊评价方法对长江下游贵池河段2005～2009年间水环境综合评价,并采用加权平均原则求取隶属等级,取得较好的效果。     

模糊评价在水闸工程管理考核中的应用

针对目前水闸工程管理考核评价中存在的问题，研究了水闸工程管理的模糊评价方法。在确定评价指标、权重分配的基础上，构建了2层模糊评价模型，利用专家定性评价结果计算各项2级指标的隶属度，采用模糊运算，依据最大隶属度原则确定评价结果。实例计算表明，水闸工程管理模糊评价得出的结果合理。因此，构建的水闸工程管理模糊评价模型是正确的。     

水环境质量系数在水质模糊评价中的应用

根据水质评价实际情况,考虑到评价过程具有较强的模糊性,将模糊数学应用到水质评价。针对模糊数学分辨率低的问题,构建水环境质量系数,并通过实例应用,表明引入水环境质量系数,评价结果能准确反映了等级间的细微差异,提高了分辨率。     

基于模糊物元模型的黑河流域水质评价

为解决水质评价中评价指标众多,存在复杂性、模糊性和不确定性等问题,采用基于指标变换值的模糊物元模型对黑河流域2013年的水质进行评价,并与传统的单因子评价法和灰色关联分析法评价结果进行对比。结果表明:模糊物元模型水质评价得出札马什克、莺落峡、高崖、正义峡、肃南5个评价断面的水质级别分别为Ⅱ、Ⅰ、Ⅱ、Ⅰ、Ⅱ;基于指标变换值的模糊物元模型得出的评价结果与灰色关联分析法是一致的,与单因子评价法的评价结果在莺落峡和正义峡两个断面上略有不同;基于指标变换值的模糊物元模型应用在黑河流域水质评价中是合理可行的,且计算简便实用。     

基于不同赋权方法的模糊综合水质评价研究

构建基于传统方法、层次分析法、变异系数法、熵值法4种权重赋值方法的模糊综合水质评价模型,对比分析4种方法的基本原理以及优缺点,以沁河和白洋淀监测断面为例,分析4种方法在不同水体中求得的权重值及应用到模糊综合评价中得到的水质评价结果的差异。研究结果表明:层次分析法既考虑了最大污染因子的影响,又考虑了指标之间的相互作用,相对而言更适合于只有1个或者2个主要污染物的水体;变异系数法与熵值法考虑了多个样本数据之间的联系,使得评价结果能更合理反映水体的综合特性,更加适合于有多个主要污染物的水体。     

基于熵权的居住小区水质模糊综合评价模型及应用

应用基于熵权的模糊数学模型对居住小区管网系统水质进行评价,可以确定管网末端不同贮水设备水质的变化情况。该模型可反映多种因子复合作用下水质的综合状况,并与实际检测系统进行连接,利用计算机编程计算,可以时刻了解水质的状况,为综合改善小区管网水质提供科学的依据。经实例验证,采用基于熵权的模糊数学模型对小区管网水质进行评价,其结果合理,方法简便,具有一定的应用价值。     

Fuzzy Conceptual Hydrological Model for Water Flow Prediction

Reliability in flow prediction is key to designing water resources projects. Over prediction may result in overdesign whereas under prediction brings about insufficient capacity solutions. While the former means insufficient use of financial resources, the latter may result in some water demand unmet. Therefore, so many techniques have been developed and used to make better flow prediction. In this study, this traditional problem is revisited in an attempt to improve the modeling performance of long used conceptual hydrological models. This is attained by incorporating fuzzy systems into a presently used conceptual model. The fuzzy integration process is carried out through the replacement of the storage elements of conceptual model by fuzzy systems. The case study undertaken has proved that the fuzzy conceptual model developed is quite competitive with ordinary conceptual model and promises improved predictions.     

鉴湖水质的模糊评判

从鉴湖水质的现状出发,提出了水质评判的必要性。采用模糊评判的方法,综合了湖泊的多项水质指标,应用模糊数学原理,采用杭州市水文监测站提供的最新资料,对鉴湖的水质等级进行评判,该方法克服了以前采用的单因子极值方式的片面性,其结果更为合理。