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Water Analysis: Emerging Contaminants and Current Issues

Environmental mass spectrometry: emerging contaminants and current issues.

Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Input determination for neural network models in water resources applications. Part 1—background and methodology

This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.

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Simulation-optimization modeling: a survey and potential application in reservoir systems operation.

Application of particle swarm optimization (PSO) is demonstrated through design of a water distribution pipeline network. PSO is an evolutionary algorithm that utilizes the swarm intelligence to achieve the goal of optimizing a specified objective function. This algorithm uses the cognition of individuals and social behaviour in the optimization process. For the optimization of water distribution system, a simulation – optimization model, called PSONET is developed and used in which the optimization is by PSO. This formulation is applied to two benchmark optimization design problems. The results are compared with the results obtained by other optimization methods. The results show that the PSO is more efficient than other optimization methods as it requires fewer objective function evaluations.

Design of water distribution networks using particle swarm optimization

There have been several attempts to use combined simulation-optimization models to solve reservoir operation problems efficiently. In many cases, complex simulation models are available, but direct incorporation of them into an optimization framework is computationally prohibitive. To overcome this problem, in this study, a backpropagation neural network is trained to approximate the simulation model developed for the Chennai city water supply problem. The neural network is used as a submodel in a Hooke and Jeeves nonlinear programming model to find “near optimal policies.” The results are further refined using the conventional simulation-optimization model.

Neural network-based simulation-optimization model for reservoir operation

This article presents a methodology for planning a modelfor the operation of a drinking water reservoir. The hedging ruledistributes deficits over a longer period of time by rationingthe supply of water and it makes the system sustainablewith a marginal reduction in supply. A methodology isdeveloped and demonstrated through a case study withthe Chennai city (India) water supply system which isa water shortage system requiring an efficient use ofwater. It is aimed at improving the reservoiroperation performance through the simulation–optimisationprocedure with the application of the hedging rule, whichis a more appropriate rule for reservoir operationunder deficit conditions. To speed up the optimisationprocess, a neural network model is developed for thesimulation of the reservoir system operation and is usedinstead of a conventional simulation model. Thecombined neural network simulation–optimisation modelis used for screening the operation policies.

Hedging Rule Optimisation for Water Supply Reservoirs System

Failures in operation of water supply reservoir systems are often unavoidable during critical hydrologic periods. The failure characteristics of such systems can be represented by performance indicators such as reliability, resilience, and vulnerability. A mixed-integer programming model for the operation of a water supply reservoir during critical periods has been presented in the literature that incorporates these performance indicators. An improved formulation of this model that represents resilience more completely is discussed herein. In addition, a set of constraints with binary integer variables are included to account for reservoir spills. The improvements achieved with the modified model is demonstrated using the same example as presented with the original model.

T. R. Neelakantan

Papers

Design of water distribution networks using particle swarm optimization

Neural network-based simulation-optimization model for reservoir operation

Hedging Rule Optimisation for Water Supply Reservoirs System

Mixed-Integer Programming Model for Reservoir Performance Optimization

A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water.