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In order to improve the estimation accuracy of stage-discharge relationship model, the back propagation neural network optimized through the genetic algorithm (GA-BP) based on information entropy was proposed. Firstly, the information entropy and hierarchical clustering were used to quickly cluster the hydrological sample data and get the optimal number of clusters. Secondly, the k-nearest neighbor algorithm was used to divide the new stage data into the most appropriate clustering categories. Finally, the river daily discharge was estimated. Some measured data collected from a hydrological station were used to test the model, and the simulation results showed that the method proposed by this paper can get higher estimation accuracy than the classical analytical model, BP neural network algorithm and GA-BP neural network algorithm, which provided a new effective method for parameter estimation of the stage-discharge relationship model. [ABSTRACT FROM AUTHOR]

Because of the complexity of some optimization problems, evolutionary and meta-heuristic algorithms are sometimes more applicable than the traditional optimization methods. Some difficulties in solving design-operation problems in the field of engineering are due to the multi-modality of the solution region of these problems. Since the design variables usually are specified as discrete variables and other continuous decision variables have to be set according to the range of the discrete ones, the possibility of trapping the final solution into some local optimum increases. In such cases, the capability of both traditional and evolutionary algorithms decreases. Thus, the development of a strategy to overcome this problem is the subject of this paper. For water utilities, one of the greatest potential areas for energy cost-savings is the effective scheduling of daily pump operations. Optimum design operation of pumping stations is a potential problem in this area that performs a wide background of solutions to this problem with different methods. Computation in all methods is driven by an objective function that includes operating and capital costs subject to various performances and hydraulic constraints. This paper achieves the optimal control and operation of an irrigation pumping station system by one of the latest tools used in optimization problems, which is the honey-bees mating optimization (HBMO) algorithm and is tested with a practical design. The RBMO algorithm with dynamic penalty function is presented and compared with two other well-known optimization tools which are the Lagrange multipliers (LM) method and genetic algorithms (GA) as well as with the previous results of the HBMO algorithm with constant penalty function for the same problem. The LM, GA and HBMO approaches simultaneously determine the least total annual cost of the pumping station and its operation. The solution includes the selection of pump type, capacity and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. In this paper, the KBMO algorithm is applied and the dynamic penalty function is tested to demonstrate the efficiency of this combination simultaneously. The results are very promising and prove the ability of combining the dynamic penalty function with the HBMO algorithm for solving combinatorial design-operation optimization problems. Application of all these models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and capability of the dynamic penalty function in the HBMO algorithm for solving complex problems of this type. [ABSTRACT FROM AUTHOR]

The recently introduced comparison of flow pattern distributions (CFPD) method for the identification, quantification and interpretation of anomalies in district metered areas (DMAs) or supply area flow time series relies, for practical applications, on visual identification and interpretation of features in CFPD block diagrams. This paper presents an algorithm for automated feature recognition in CFPD analyses of DMA or supply area flow data, called CuBOid, which is useful for objective selection and analysis of features and automated (pre-)screening of data. As such, it can contribute to rapid identification of new leakages, unregistered changes in valve status or network configuration, etc., in DMAs and supply areas. The method is tested on synthetic and real flow data. The obtained results show that the method performs well in synthetic tests and allows an objective identification of most anomalies in flow patterns in a real life dataset. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta-heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine-tuned compared to many other algorithms, which is appealing for its practical application as an extensive parametercalibration process is typically computationally very demanding. [ABSTRACT FROM AUTHOR]

Flood control operation in a multi-reservoir system is a multi-criteria decision-making (MCDM) problem, in which the considered criteria are often correlated with each other. In this paper, we propose an MCDM model for reservoir flood control operation to deal with correlation among criteria. Considering the flood control safety of reservoirs and downstream protected regions, we establish the hierarchical structure of the criterion system. We use the principal component analysis method to eliminate the correlation, and transform the original criterion system into an independent comprehensive criterion system. The comprehensive decision matrix coupled with the weight vector obtained by the improved entropy weight method serves as the input to TOPSIS method, fuzzy optimum method, and fuzzy matter-element method, by which we determine the ranking order of the alternatives. We apply the proposed model to a cascade system of reservoirs at the Daduhe River basin in China. The results show that the dimensionality of the criterion system is reduced and the correlation among criteria is eliminated simultaneously, and the ranking order of the alternatives is reasonable. The proposed model provides an effective way to deal with correlation among criteria, and can be extended to wider applications in many other MCDM problems. [ABSTRACT FROM AUTHOR]

In this paper, the main features and performances of a new numerical scheme, ILILPM (Improved Locally and Instantaneously Linearized Parabolic Model) are described. ILILPM is an improved version of the Parabolic and Backwater (PAB) and Linearized Parabolic Model (LPM) schemes, proposed in literature for the approximate solution of the Parabolic Wave model. The algorithm presented is able to take into account transcritical flow regime and transitions from free-surface to pressurized flow in tree-like channel networks. Due to its unconditional stability, the model allows large computational time steps, leading to very fast simulations during transients for a class of flow conditions larger than those solved by the parent schemes. The model is demonstrated by comparing its results with experimental observations and with the results provided by the numerical solution of the full De Saint-Venant equations. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of an extended simplification algorithm of water distribution network models for the purpose of inclusion in the online optimisation strategy for energy and leakage management in water distribution systems. Whereas the previously proposed reduced model represented accurately the original hydraulic water network characteristics, the energy distribution in the simplified model was not preserved. This could cause a situation where the pump speed required to satisfy specified minimum pressure constraints is different for the reduced model and the original model. This problem has been identified, and an appropriate modification to the simplification algorithm has been introduced. The idea comprises introduction of the energy audit of the water network and the calculation of new minimum service pressure constraints for the simplified model. The approach allows the preservation of both hydraulic and energetic characteristics of the original water network and therefore meets the requirements of the online optimisation strategy. Suitability of the proposed approach is evaluated via a case study. The modern parallel programming implementation allowed water network models consisting of several thousand elements to be reduced within 2 min with an average relative accuracy of less than 2% in terms of tanks flows. [ABSTRACT FROM AUTHOR]

This paper extends the application of Constrained Ant Colony Optimization Algorithms (CACOAs) to optimal operation of multi-reservoir systems. Three different formulations of the constrained Ant Colony Optimization (ACO) are outlined here using Max-Min Ant System for the solution of multireservoir operation problems. In the first two versions, called Partially Constrained ACO algorithms, the constraints of the multi-reservoir operation problems are satisfied partially. In the third formulation, all the constraints of the underlying problem are implicitly satisfied by the provision of tabu lists to the ants which contain only feasible options. The ants are, therefore, forced to construct feasible solutions and hence the method is referred to as a Fully Constrained ACO algorithm. The proposed constrained ACO algorithms are formulated for both possible cases of taking storage/release volumes as the decision variables of the problem. The proposed methods are used to optimally solve the well-known problems of four- and ten-reservoir operations and the results are presented and compared with those of the conventional unconstrained ACO algorithm and existing methods in the literature. The results indicate the superiority of the proposed methods over conventional ACOs and existing methods to optimally solve large scale multi-reservoir operation problems. [ABSTRACT FROM AUTHOR]

This paper presents a simulation--optimization-based method for identification of contamination source characteristics in a water distribution system using filtered data from threshold-based binary water quality signals. The effects of quality and quantity of the data on the accuracy of the source identification methodology are investigated. This study also addresses the issue of non-uniqueness in contaminant source identification under various data availability conditions. To establish the robustness and applicability of the methodology, numerous scenarios are investigated for a wide range of contamination incidents associated with two different networks. Results indicate that, even though use of lower resolution sensors lead to more non-unique solutions, the true source location is always included among these solutions. [ABSTRACT FROM AUTHOR]

Digital Terrain Models (DTMs) represent an essential source of information that can allow the behaviour of the urban floodplain, and its interactions with the drainage system, to be examined, understood and predicted. Typically, such data are obtained via Light Detection and Ranging (LiDAR). If a DTM does not contain adequate representation of urban features the results from the modelling efforts can be. This is due to the fact that urban environments contain variety of features, which can have functions of storing and/or diverting flows during flood events. The work described in this paper concerns further improvements of a LiDAR filtering algorithm which was discussed in a previous work. The key characteristics of this improved algorithm are: ability to deal with buildings, detect elevated road and represent them accordance to reality and deal with bridges and riverbanks. The algorithm was tested using a real-life data from a case study of Kuala Lumpur. The results have shown that the newly developed MPMA2 algorithm has better capabilities of identifying some of the features that are vital for urban flood modelling applications than any of the currently available algorithms and it leads to better agreement between simulated and observed flood depths and flood extents. [ABSTRACT FROM AUTHOR]

An assessment has been carried out to study the performance of seven different LiDAR filtering algorithms and to evaluate their suitability for urban flood modeling applications. It was found that none of these algorithms can be regarded as fully suitable to support such work in its present form. The paper presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modeling framework. The existing progressive morphological filtering algorithm was modified to incorporate buildings with basement, passage buildings and solid buildings and its value was demonstrated on a case study from Kuala Lumpur, Malaysia. The model results were analyzed and compared against recorded data in terms of flood depths, flood extents and flood velocities. The difference in flood depths of approximately 40% was observed between a model that uses a DTM modified by the progressive morphological filtering algorithm and the predictions of other models. The overall results suggest that incorporation of building basements within the DTM can lead to a significant difference in the model results with a tendency towards overestimation for those models which do not incorporate such a feature. [ABSTRACT FROM AUTHOR]

The scarcity of water compared with the abundance of land constitutes the main drawback within agricultural production. Besides the improvement of irrigation techniques a task of primary importance is solving the problem of intra-seasonal irrigation scheduling under limited seasonal water supply. An efficient scheduling algorithm has to take into account the crops' response to water stress at different stages throughout the growing season. Furthermore, for large-scale planning tools compact presentations of the relationship between irrigation practices and grain yield, such as crop water production functions, are often used which also rely on an optimal scheduling of the considered irrigation systems. In this study, two new optimization algorithms for single-crop intraseasonal scheduling of deficit irrigation systems are introduced which are able to operate with general crop growth simulation models. First, a tailored evolutionary optimization technique (EA) searches for optimal schedules over a whole growing season within an open-loop optimization framework. Second, a neuro-dynamic programming technique (NDP) is used for determining optimal irrigation policy. In this paper, different management schemes are considered and crop-yield functions generated with both the EA and the NDP optimization algorithms compared. [ABSTRACT FROM AUTHOR]

The main reason for applying evolutionary algorithms in multi-objective optimization problems is to obtain near-optimal nondominated solutions/Pareto fronts, from which decision-makers can choose a suitable solution. The efficiency of multi-objective optimization algorithms depends on the quality and quantity of Pareto fronts produced by them. To compare different Pareto fronts resulting from different algorithms, criteria are considered and applied in multi-objective problems. Each criterion denotes a characteristic of the Pareto front. Thus, ranking approaches are commonly used to evaluate different algorithms based on different criteria. This paper presents three multi-objective optimization methods based on the multi-objective particle swarm optimization (MOPSO) algorithm. To evaluate these methods, bi-objective mathematical benchmark problems are considered. Results show that all proposed methods are successful in finding near-optimal Pareto fronts. A ranking method is used to compare the capability of the proposed methods and the best method for further study is suggested. Moreover, the nominated method is applied as an optimization tool in real multi-objective optimization problems in multireservoir system operations. A new technique in multi-objective optimization, called warm-up, based on the PSO algorithm is then applied to improve the quality of the Pareto front by single-objective search. Results show that the proposed technique is successful in finding an optimal Pareto front. [ABSTRACT FROM AUTHOR]

The calibration of hydraulic models of water distribution networks (WDN) is of preeminent importance for their analysis and management. It is usually achieved by solving a constrained optimization problem based on some priors on decision variables and the demand-driven simulation of the entire network, given the observations of some hydraulic status variables (i.e. typically nodal heads and sometimes pipe flows). This paper presents a framework to perform the calibration of pipe hydraulic resistances considering two main issues: (i) the enhancements of WDN simulation models allowing us to simplify network topology with respect to serial nodes/trunks and/or to account for a more realistic representation of distributed demands and (ii) a different formulation of the calibration problem itself. Depending on the available measurements, the proposed calibration strategy reduces the hydraulic simulation model size and can permit the decomposition of the network. On the one hand, such a procedure allows for numerical and computational advantages, especially for large size networks. On the other hand, it allows a prompt analysis of observability of calibration decision variables based on actual observations and might help identifying those pipes (i.e. hydraulic resistances) which are more important for the whole network behaviour. [ABSTRACT FROM AUTHOR]

Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation--optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes. [ABSTRACT FROM AUTHOR]

Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple ('Quick Look') to complex and computing-intensive ('Hi-Fi'). The 'Pseudo NWS PPS' option allows close comparison with the algorithm used operationally by the US National Weather Service. The 'Custom' algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications. [ABSTRACT FROM AUTHOR]

Valve and pump shut-off in water pipeline systems lead to transient flow. This flow is a complex phenomenon and is potentially a very serious problem causing extra dynamic pressure in the system. Within the last few decades, the evolutionary and meta-heuristic algorithms, such as genetic algorithms, simulated annealing. More recently, however, ant-colony optimization algorithms have received considerable attention. In this paper the procedure and application of the ant-colony optimization algorithm to the design of a water supply pipeline system,, considering dynamic pressures arising from valve closure, is presented. A simulation- optimization interaction loop (SOIL) is defined that cycles between the steady-state and transient flow modules to describe the hydraulics of the pipeline and ant colony optimization algorithm. A hydraulic simulation module is coupled with the ant colony optimization algorithm to form an efficient and powerful software program which locates the pumping stations at any possible or predefined locations while optimizing their specifications, along with pipe diameters, at each decision point. The model may equally regard or disregard the dynamic pressures. Two examples are provided to illustrate the proposed methodology which is limited to the solution of any gravity and/or forced water supply pipeline which is typical for water supply systems. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions. [ABSTRACT FROM AUTHOR]

Heuristic search techniques are highly flexible, though they represent computationally intensive optimization methods that may require thousands of evaluations of expensive objective functions. This paper integrates MODSIM, a generalized river basin network flow model, a particle swarm optimization (PSO) algorithm and artificial neural networks into a modeling framework for optimum water allocations at basin scale. MODSIM is called in the PSO model to simulate a river basin system operation and to evaluate the fitness of each set of selected design and operational variables with respect to the model's objective function, which is the minimization of the system's design and operational cost. Since the direct incorporation of MODSIM into a PSO algorithm is computationally prohibitive, an ANN model as a meta-model is trained to approximate the MODSIM modeling tool. The resulting model is used in the problem of optimal design and operation of the upstream Sirvan river basin in Iran as a case study. The computational efficiency of the model makes it possible to analyze the model performance through changing its parameters so that better solutions are obtained compared to those of the original PSO-MODSIM model. [ABSTRACT FROM AUTHOR]

The honey-bee mating process is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to extract the linear monthly operation rules of reservoirs for both irrigation and hydropower purposes. The release rules for each month are considered as a linear function of the reservoir past-month-end storage as well as current monthly inflow to the reservoir. In such a case, the decision variables are 36 for each problem and are set so that water supply deficits are minimized. In both irrigation and hydropower purposes, 60-480 months are considered and results are compared to those from the nonlinear programming solver of the LINGO 8.0 software. The approach and the rules tend to be very promising and denote the capability of the proposed HBMO algorithm in solving reservoir operation problems. Furthermore, the results indicated that, by using the near-optimal solution from the HBMO as a starting point for the NLP solver, the obtained objective function value was enhanced significantly and a better local optimum was found. [ABSTRACT FROM AUTHOR]

This paper is intended to serve as an introduction to the POWADIMA research project, whose objective was to determine the feasibility and efficacy of introducing real-time, near-optimal control for water-distribution networks. With that in mind, its contents include the current state-of-the-art and some of the difficulties that would need to be addressed if the goal of near-optimal control was to be achieved, Subsequently, the approach adopted is outlined, together with the reasons for the choice. Since it would be somewhat impractical to use a conventional hydraulic simulation model for real-time, near-optimal control, the methodology includes replicating the model by an artificial neural network which, computationally, is far more efficient. Thereafter, the latter is embedded in a dynamic genetic algorithm, designed specifically for real-time use. In this way, the near-optimal control settings to meet the current demands and minimize the overall pumping costs up to the operating horizon can be derived. The programme of work undertaken in achieving this end is then described. By way of conclusion, the potential benefits arising from implementing the control system developed are briefly reviewed, as are the possibilities of using the same approach for other application areas. [ABSTRACT FROM AUTHOR]

This paper describes a simple mathematical technique that uses a genetic algorithm and least squares optimization to obtain a functional approximation (or computer program) for a given data set. Such an optimal functional form is derived from a pre-defined general functional formulation by selecting optimal coefficients, decision variable functions, and mathematical operators. In the past, functional approximations have routinely been obtained through the use of linear and non-linear regression analysis. More recent methods include the use of genetic algorithms and genetic programming. An example application based on a data set extracted from the commonly used Moody diagram has been used to demonstrate the utility of the proposed method. The purpose of the application was to determine an explicit expression for friction factor and to compare its performance to other available techniques. The example application results in the development of closed form expressions that can be used for evaluating the friction factor for turbulent pipe flow. These expressions compete well in accuracy with other known methods, validating the promise of the proposed method in identifying useful functions for physical processes in a very effective manner. The proposed method is simple to implement and has the ability to generate simple and compact explicit expressions for a given response function. [ABSTRACT FROM AUTHOR]

In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated software effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme, a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system's library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find the optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Using these modules the steps that yield the best solution for a problem from the available hydrographic data under a set of specified conditions are explained. This is further elucidated by an illustrative case study which shows the applicability of the present CBEM system under realistic conditions. This case deals with the simulation of salinity distribution in the Tay estuary (Scotland, UK). Keywords case based reasoning; estuary modelling; genetic algorithm; IT systems [ABSTRACT FROM AUTHOR]

This paper implements the inverse approach for forecasting hydrological time series in an efficient way using a micro-GA (mGA) search engine. The inverse approach is based on chaos theory and it involves: (1) calibrating the delay time ((), embedding dimension (m) and number of nearest neighbors (k) simultaneously using a single definite criterion, namely optimum prediction accuracy, (2) verifying that the optimal parameters have wider applicability outside the scope of calibration, and (3) demonstrating that chaotic behaviour is present when optimal parameters are used in conjunction with existing system characterization tools. The first stage is conducted efficiently by coupling the Nonlinear Prediction (NLP) method with mGA using a lookup facility to eliminate costly duplicate NLP evaluations. The mGA-NLP algorithm is applied to a theoretical chaotic time series (Mackey–Glass) and a real hydrological time series (Mississippi river flow at Vicksburg) to examine its efficiency. Results show that: (1) mGA is capable of producing comparable or superior triplets using only up to 5% of the computational effort of all possible points in the search space, (2) the lookup facility is very cost-effective because only about 50% of the triplets generated by mGA are distinct, (3) mGA seems to produce more robust solutions in the sense that the record length required to achieve a stable optimum triplet is much shorter, and (4) the prediction accuracy is not sensitive to the parameter k. It is sufficient to use k = 10 in future studies. In this way, the 3D search space could be reduced to a much smaller 2D search space of m and τ. Keywords chaotic time series; inverse approach; micro genetic algorithm; nonlinear prediction [ABSTRACT FROM AUTHOR]

The focus of the paper is on demonstrating how it is possible to automate complex CFD simulations using scripting language around and within the structure of the CFD command files. To illustrate this, the concept of an atmospheric pollution case is used and, more specifically, that of a water treatment plant. The code that is used is CFX-5 with PERL as a scripting 'language'. The simulation of the factory atmospheric environment and its fluctuating conditions are fully automated. The simulation is based on a pre-defined generic CFD model, for which initial conditions, boundary conditions and source terms of atmospheric pollutant release are written automatically by the scripts using data recorded by measuring devices and stored on computers every half an hour as the simulation runs. When the correct amount of time has elapsed, the simulation pauses and the script updates the set-up using the newly recorded data. It then proceeds further, restarting from the appropriate result files. At each pause, a HTML report is also produced, which contains pictures of the area and summary tables. If a suitable criterion is defined in the post-treatment algorithm, such as a critical concentration for example, an alarm bell can be started, so that the technician knows the simulation has found a potential problem within the large domain that is thus monitored. The implications of this work are numerous. Firstly, non-CFD experts can run and use results from a CFD simulation without having to implement the models, run the simulation or fully understand the intricacy of the physics and mathematics that it contains. Going further, it is even possible to parametrize the generic model set-up, e.g. the domain dimensions or the location of emission sources, to make the case more flexible. Running the application remotely is also possible, using a web browser to submit the necessary input to the CFD code. Secondly, a very wide area can be monitored numerically, which would not be commercially viable with physical devices and field monitoring campaigns. Thirdly, such a simulation can be used to learn the general behaviour of, and the potential problems associated with, the region of interest and eventually set up a response plan to any given situation known to cause discomfort or form a health hazard to the neighbourhood. This feedback can be used to improve the operation of the plant and its safety, but also to enhance the model set-up for future simulations. [ABSTRACT FROM AUTHOR]

This paper deals with the application of genetic algorithms to the operation of a water resource system in real time. A genetic algorithm was developed and applied to solve an optimization model for the operation of the system responsible for the water supply of Matsuyama City, in Japan. For comparison purposes, the same model was solved by a technique based on calculus and the Shuffled Complex Evolution Algorithm. The general characteristics of the algorithms and the results from simulations carried out for various conditions are presented. Genetic algorithms appear to be effective tools for real-time reservoir operation. [ABSTRACT FROM AUTHOR]