Multi‐objective particle swarm optimization for generating optimal trade‐offs in reservoir operation

A multi‐objective particle swarm optimization (MOPSO) approach is presented for generating Pareto‐optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist‐mutation (EM) operator are introduced. The proposed EM‐MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM‐MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi‐objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM‐MOPSOs yield a trade‐off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision‐making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi‐objective water resources and hydrology problems. Copyright © 2007 John Wiley & Sons, Ltd.     

Multireservoir system optimization in the Han River basin using multi‐objective genetic algorithms

In this study, NSGA‐II is applied to multireservoir system optimization. Here, a four‐dimensional multireservoir system in the Han River basin was formulated. Two objective functions and three cases having different constraint conditions are used to achieve nondominated solutions. NSGA‐II effectively determines these solutions without being subject to any user‐defined penalty function, as it is applied to a multireservoir system optimization having a number of constraints (here, 246), multi‐objectives, and infeasible initial solutions. Most research by multi‐objective genetic algorithms only reveals a trade‐off in the objective function space present, and thus the decision maker must reanalyse this trade‐off relationship in order to obtain information on the decision variable. Contrastingly, this study suggests a method for identifying the best solutions among the nondominated ones by analysing the relation between objective function values and decision variables. Our conclusions demonstrated that NSGA‐II performs well in multireservoir system optimization having multi‐objectives. Copyright © 2005 John Wiley & Sons, Ltd.     

Automatic calibration of HEC‐HMS using single‐objective and multi‐objective PSO algorithms

This study presents single‐objective and multi‐objective particle swarm optimization (PSO) algorithms for automatic calibration of Hydrologic Engineering Center‐ Hydrologic Modeling Systems rainfall‐runoff model of Tamar Sub‐basin of Gorganroud River Basin in north of Iran. Three flood events were used for calibration and one for verification. Four performance criteria (objective functions) were considered in multi‐objective calibration where different combinations of objective functions were examined. For comparison purposes, a fuzzy set‐based approach was used to determine the best compromise solutions from the Pareto fronts obtained by multi‐objective PSO. The candidate parameter sets determined from different single‐objective and multi‐objective calibration scenarios were tested against the fourth event in the verification stage, where the initial abstraction parameters were recalibrated. A step‐by‐step screening procedure was used in this stage while evaluating and comparing the candidate parameter sets, which resulted in a few promising sets that performed well with respect to at least three of four performance criteria. The promising sets were all from the multi‐objective calibration scenarios which revealed the outperformance of the multi‐objective calibration on the single‐objective one. However, the results indicated that an increase of the number of objective functions did not necessarily lead to a better performance as the results of bi‐objective function calibration with a proper combination of objective functions performed as satisfactorily as those of triple‐objective function calibration. This is important because handling multi‐objective optimization with an increased number of objective functions is challenging especially from a computational point of view. Copyright © 2012 John Wiley & Sons, Ltd.     

Projection Pursuit Evaluation Model: Optimizing Scheme of Crop Planning for Agricultural Sustainable Development and Soil Resources Utilization

Planting structure influences the economic, social, and ecological benefits of crop farming as well as the use efficiency of water and arable land resources, and so crop planning (CP) benefits for agricultural sustainable development and soil resources utilization. The projection pursuit evaluation (PPE) model is put forward to solve the problem of selecting an optimizing scheme for CP by considering the indices of water‐saving and economic, social, and ecological benefits. The real‐coding‐based accelerating genetic algorithm (RAGA) is introduced to accelerate the calculation process. The model can translate multi‐indices into a single index by transforming high‐dimensional data to low‐dimensional space, which helps evaluate CP optimizing schemes. For example, the model is used to evaluate and select an optimal scheme of CP in the middle reaches of the Heihe mainstream basin in the arid area of northwest China. According to four criteria (high efficiency of resources use, economic rationality, social equity, and ecological security) 19 indices were chosen to evaluate 12 optimizing schemes of four kinds (economic‐benefit, food‐security, ecological‐benefit, and water‐saving programs) in 2006, 2020, and 2030. The result shows that, in the 3 years, the water‐saving program is always the optimized scheme in an arid region with water deficiency and fragile ecology. The evaluated results match up to the developmental conditions of crop farming in recent years. Moreover, the direction of the optimal projection could reflect the weight and orientation of indices objectively and accurately.     

On the use of multi‐algorithm,genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model

With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd.     

考虑抗震性约束的建筑空间结构优化

当前对建筑空间结构进行优化时,所采用的算法趋同性高,无法实现多目标种群优化,易陷入局部最优解,存在寻优质量低、优化成本高、抗震性能低的问题。针对上述问题,提出一种基于改进粒子群算法的建筑空间结构优化方法。该方法以空间结构的抗震性能、工程造价为优化目标,来优化建立建筑空间结构设计;引入多子群协同进化机制解决建筑空间结构抗震优化设计中多目标间的种群优化问题,同时引入外部档案和精英学习策略改进粒子群算法,筛选出满足目标函数的最优设计方案,完成抗震性约束的建筑空间结构优化。实验结果表明:所提方法对建筑空间结构优化时的特点为寻优质量高、优化成本低、抗震性能高。     

Optimized earthquake response of multi‐storey buildings with seismic isolation at various elevations

The response of multi‐storey structures can be controlled under earthquake actions by installing seismic isolators at various storey levels. By vertically distributing isolation devices at various elevations, the designer is provided with numerous options to appropriately adjust the seismic performance of a building. However, introducing seismic isolators at various storey levels is not a straightforward task, as it may lead to favourable or unfavourable structural behaviour depending on a large number of factors. As a consequence, a rather chaotic decision space of seismic isolation configurations arises, within which a favourable solution needs to be located. The search for favourable isolators' configurations is formulated in this work as a single‐objective optimization task. The aim of the optimization process is to minimize the maximum floor acceleration of the building under consideration, while constraints are specified to control the maximum interstorey drift, the maximum base displacement and the total seismic isolation cost. A genetic algorithm is implemented to perform this optimization task, which selectively introduces seismic isolators at various elevations, in order to identify the optimal configuration for the isolators satisfying the pre‐specified constraints. This way, optimized earthquake response of multi‐storey buildings can be obtained. The effectiveness of the proposed optimization procedure in the design of a seismically isolated structure is demonstrated in a numerical study using time‐history analyses of a typical six‐storey building. Copyright © 2012 John Wiley & Sons, Ltd.     

Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm

Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.     

Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis

The hydrological response characteristics for the catchments in the Republic of Korea are related to a strong seasonality in the rainfall and streamflow distributions with distinct wet and dry seasons. This study aims to improve a model's ability to predict streamflows by minimizing information loss from the available data during the calibration processes. This study assesses calibration techniques incorporating a multi‐objective approach and seasonal calibration. The lumped conceptual rainfall–runoff model IHACRES was applied to selected catchments in Korea. The model was calibrated based on three different methods: the classical approach using a single performance statistic (the single‐objective method), the multi‐objective approach (the multi‐objective method (I)) and the combined approach incorporating multi‐objective and seasonal calibrations (the multi‐objective method (II)). In the multi‐objective approach, the ‘best fit’ models in the calibration period were selected by considering the trade‐offs among multiple statistics. During seasonal calibration, the calibration period was divided into four seasons to investigate whether these calibrated models can improve the model performance with regards to seasonal climate, rainfall and streamflow distributions. The adequacy of the three different calibration methods was assessed through comparison of the variability of model performance in high and low flows and water balance for the entire period and for each seasonal period. The multi‐objective methods yielded more accurate and consistent predictions for high and low flows and water balance simultaneously, compared to the single‐objective method. In particular, the multi‐objective method (II) produces the best modelling capacity to capture the non‐stationary nature of the hydrological response under different climate conditions. The pattern of improvement with the multi‐objective method (II) was generally consistent through the seasons, with the exception of the winter period in the regions partially affected by snow. This exception is due to a potential limitation of the IHACRES model in reflecting the impact of snow on the catchment hydrology. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimal seismic design of steel frame buildings based on life cycle cost considerations

A multi‐objective optimization procedure is presented for designing steel moment resisting frame buildings within a performance‐based seismic design framework. Life cycle costs are considered by treating the initial material costs and lifetime seismic damage costs as two separate objectives. Practical design/construction complexity, important but difficult to be included in initial cost analysis, is taken into due account by a proposed diversity index as another objective. Structural members are selected from a database of commercially available wide flange steel sections. Current seismic design criteria (AISC‐LRFD seismic provisions and 1997 NEHRP provisions) are used to check the validity of any design alternative. Seismic performance, in terms of the maximum inter‐storey drift ratio, of a code‐verified design is evaluated using an equivalent single‐degree‐of‐freedom system obtained through a static pushover analysis of the original multi‐degree‐of‐freedom frame building. A simple genetic algorithm code is used to find a Pareto optimal design set. A numerical example of designing a five‐storey perimeter steel frame building is provided using the proposed procedure. It is found that a wide range of valid design alternatives exists, from which a decision maker selects the one that balances different objectives in the most preferred way. Copyright © 2003 John Wiley & Sons, Ltd.     

Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study

In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.     

Assessment of the adequacy of the regional relationships between catchment attributes and catchment response dynamics,calibrated by a multi‐objective approach

This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi‐objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over‐parameterization and data requirements. The IHACRES rainfall‐runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi‐objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall‐runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi‐objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single‐objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi‐objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single‐objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single‐objective measure. The parameters calibrated by the single‐objective approach decreased the predictability of the regional models. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion,and residual static computation

We compare the performances of four stochastic optimisation methods using four analytic objective functions and two highly non‐linear geophysical optimisation problems: one‐dimensional elastic full‐waveform inversion and residual static computation. The four methods we consider, namely, adaptive simulated annealing, genetic algorithm, neighbourhood algorithm, and particle swarm optimisation, are frequently employed for solving geophysical inverse problems. Because geophysical optimisations typically involve many unknown model parameters, we are particularly interested in comparing the performances of these stochastic methods as the number of unknown parameters increases. The four analytic functions we choose simulate common types of objective functions encountered in solving geophysical optimisations: a convex function, two multi‐minima functions that differ in the distribution of minima, and a nearly flat function. Similar to the analytic tests, the two seismic optimisation problems we analyse are characterised by very different objective functions. The first problem is a one‐dimensional elastic full‐waveform inversion, which is strongly ill‐conditioned and exhibits a nearly flat objective function, with a valley of minima extended along the density direction. The second problem is the residual static computation, which is characterised by a multi‐minima objective function produced by the so‐called cycle‐skipping phenomenon. According to the tests on the analytic functions and on the seismic data, genetic algorithm generally displays the best scaling with the number of parameters. It encounters problems only in the case of irregular distribution of minima, that is, when the global minimum is at the border of the search space and a number of important local minima are distant from the global minimum. The adaptive simulated annealing method is often the best‐performing method for low‐dimensional model spaces, but its performance worsens as the number of unknowns increases. The particle swarm optimisation is effective in finding the global minimum in the case of low‐dimensional model spaces with few local minima or in the case of a narrow flat valley. Finally, the neighbourhood algorithm method is competitive with the other methods only for low‐dimensional model spaces; its performance sensibly worsens in the case of multi‐minima objective functions.     

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.     

Multi‐level design model and genetic algorithm for structural control system optimization

The integrated optimum problem of structures subjected to strong earthquakes and wind excitations, optimizing the number of actuators, the configuration of actuators and the control algorithms simultaneously, is studied. Two control algorithms, optimal control and acceleration feedback control, are used as the control algorithms. A multi‐level optimization model is proposed with respect to the solution procedure of the optimum problem. The characteristics of the model are analysed, and the formulation of each suboptimization problem at each level is presented. To solve the multi‐level optimization problem, a multi‐level genetic algorithm (MLGA) is proposed. The proposed model and MLGA are used to solve two multi‐level optimization problems in which the optimization of the number of actuators, the positions of actuators and the control algorithm are considered in different levels. In problem 1, an example structure is excited by strong wind, and in problem 2, an example structure is subjected to strong earthquake excitation. Copyright © 2001 John Wiley & Sons, Ltd.     

Multi‐scale assimilation of root zone soil water predictions

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi‐scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi‐scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi‐scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi‐scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd.     

Advantages of the analytic element method for the solution of groundwater management problems

In the simulation‐optimization approach, a coupled optimization and groundwater flow/transport model is used to solve groundwater management problems. The efficiency of the numerical method, which is used to simulate the groundwater flow, is one the major reason to obtain the best solution for a management problem. This study was carried out to examine the advantages of the analytic element method (AEM) in the simulation‐optimization approach, for the solution of groundwater management problems. For this study, the AEM and finite difference method (FDM) based flow models were developed and coupled with the particle swarm optimization (PSO)‐based optimization model. Furthermore, the AEM‐PSO and FDM‐PSO models developed were applied in hypothetical as well as real field conditions to address groundwater management problems and the results were compared. For the real field situation, the models developed were applied to the Dore River basin in France to minimize the installation and operational cost of new pumping wells taking the location and discharge of the pumping wells as decision variables. The constraints of the problem were identified with the help of stakeholders and water authority officials. The AEM flow model was developed to facilitate the management model particularly when at each iteration, the optimization model calls for a simulation model to calculate the values of groundwater heads. The results show that, at some points, the AEM‐PSO model is efficient in identifying the optimal location of wells and consequently results in optimal costs, sometimes difficult when using the FDM. Copyright © 2011 John Wiley & Sons, Ltd.     

Seismic design of RC structures: A critical assessment in the framework of multi‐objective optimization

The assessment of seismic design codes has been the subject of intensive research work in an effort to reveal weak points that originated from the limitations in predicting with acceptable precision the response of the structures under moderate or severe earthquakes. The objective of this work is to evaluate the European seismic design code, i.e. the Eurocode 8 (EC8), when used for the design of 3D reinforced concrete buildings, versus a performance‐based design (PBD) procedure, in the framework of a multi‐objective optimization concept. The initial construction cost and the maximum interstorey drift for the 10/50 hazard level are the two objectives considered for the formulation of the multi‐objective optimization problem. The solution of such optimization problems is represented by the Pareto front curve which is the geometric locus of all Pareto optimum solutions. Limit‐state fragility curves for selected designs, taken from the Pareto front curves of the EC8 and PBD formulations, are developed for assessing the two seismic design procedures. Through this comparison it was found that a linear analysis in conjunction with the behaviour factor q of EC8 cannot capture the nonlinear behaviour of an RC structure. Consequently the corrected EC8 Pareto front curve, using the nonlinear static procedure, differs significantly with regard to the corresponding Pareto front obtained according to EC8. Furthermore, similar designs, with respect to the initial construction cost, obtained through the EC8 and PBD formulations were found to exhibit different maximum interstorey drift and limit‐state fragility curves. Copyright © 2007 John Wiley & Sons, Ltd.     

Impact of water resource exploitation on the hydrology and water storage in Baiyangdian Lake

Water resources development and exploitation are critical for a viable and sustainable modern human society. Unfortunately, however, there is a considerable water storage depletion and environmental degradation in especially (semi)‐arid river basins due to the forces of population growth, urbanization, industrialization and intensive agricultural irrigation. Addressing water storage depletion is not only a question of research, but is very much a question of developing appropriate countermeasures to preserve valuable/fragile ecological systems. As one such effort, this study analyzes the hydrology and storage in Baiyangdian Lake as affected by water resources development and exploitation in the Baiyangdian Lake Catchment of Northern China. Three models, WetSpass (Water and Energy Transfer between Soil, Plants and the Atmosphere under quasi‐Steady State), WATBUD (Water Budget) and MODFLOW (USGS three‐dimensional finite‐difference groundwater flow model) were used in combination to simulate the hydrogeologic conditions in the lake catchment for 1956–2008. The model‐calibrated values are in good agreement with the measured values, with R² > 0·8 and RMSE < 10% of measured values. Runoff, the primary source of water for the lake storage, has steadily declined due mainly to multiple dam construction and reservoir impoundments in the headwater valleys and rivers in the catchment. In addition to dwindling runoff, groundwater levels have declined considerably due to over‐abstraction, mainly for agricultural irrigation. Additionally, evaporation or evapotranspiration is increasing in the lake catchment due to rising temperatures. The worsening hydrological conditions, amid the harsh semi‐arid climate, have resulted in considerable depletion of the storage and hydrology of Baiyangdian Lake. Sustainable countermeasures like agricultural water‐saving and infusion of external water (e.g., via by the South–North Water Transfer Project) could be a viable option for preserving not only the hydrology of the lake catchment, but also storage in Baiyangdian Lake. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization

Abstract

An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.

Citation Lerma, N., Paredes-Arquiola, J., Andreu, J., and Solera, A., 2013. Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58 (4), 797–812.