Made available in DSpace on 2017-07-10T18:13:28Z (GMT). No. of bitstreams: 1 Cristina Aparecida Benelle.pdf: 285891 bytes, checksum: 2c3bc6c8c3e76bcfd74a142405d31ec5 (MD5) Previous issue date: 2010-09-27 / This study aimed to investigate regulatory mechanisms of an abundance of piscivorous fish in Neotropical reservoirs, and the effects of different types of operating systems. For the study were collected bimonthly from July 2003 to January 2010 fish and limnological variables, sampled at seven locations, four in Salto Santiago reservoir and three in the Salto Osório reservoir. The sampling of fish were taken with gillnets with different mesh size (between 2.4 and 16.0 cm) and trammel net (between 6.0 and 8.0 cm) opposite knots, exposed for 24 h. Abiotic variables such as water temperature, dissolved oxygen, conductivity, pH and water transparency were measured in situ with portable equipment, for the other analysis was collected with the aid of water bottle of Van Dorn, used in subsequent analysis performed in the laboratory. In order to evaluate the effect between the biotic (CPUE transformed into log (x +1)), abiotic, operating systems and sampling in the marginal areas, data were submitted to multiple regression (backward stepwise) with variables indicator, and used the correlation matrix between the predictor variables in order to check possible sources of multicollinearity. Chemical analysis performed was found that the species showed different patterns of abundance in the reservoirs, as well as their prey, which was possibly a reflection of operating systems, also showing an increase in the abundance of species in the marginal area. / Esse trabalho teve por objetivo investigar mecanismos reguladores da abundância populacional Hoplias aff. malabaricus em reservatórios neotropicais, bem como os efeitos dos diferentes tipos de sistemas de operação. Para iss os peixes e variáveis limnológicas foram coletados bimestralmente entre julho de 2003 à janeiro de 2010 de peixes e variáveis limnológicas, amostrados em sete locais, sendo quatro no reservatório de Salto Santiago e três no reservatório de Salto Osório. As amostragens de peixes ocorreram com redes de espera simples de malhas 2,4 a 16 cm e tresmalhos de 6 a 8 cm de entre nós não adjacentes, expostas por 24 h. Variáveis abióticas como: temperatura da água, oxigênio dissolvido, condutividade elétrica, pH e transparência da água foram medidos "in situ" com equipamentos portáteis e, para as demais análises, a água foi coletada com auxílio de garrafa de Van Dorn, utilizada em análises posteriores efetuadas em laboratório. Com a finalidade de avaliar o efeito entre os fatores bióticos (CPUE transformados em log(x+1)), abióticos, sistemas de operação e amostragens na área marginal, os dados foram submetidos ao procedimento de regressão múltipla (backward stepwise) com variáveis indicadoras, sendo usada a matriz de correlação entre as variáveis preditoras a fim de verificar possíveis fontes de multicolinearidade. Através das análises realizadas foi verificado que a espécie apresentou diferentes padrões de abundância nos reservatórios, assim como suas presas, o que possivelmente foi reflexo dos sistemas de operação, mostrando também um incremento na abundância da espécie na região marginal.

Ce travail de recherche vise à optimiser la procédure opérationnelle des centrales hydroélectriques en cascade afin de les utiliser efficacement pour la production d’électricité et l’irrigation. Le défi consistait à trouver le modèle le plus réaliste basé sur la caractéristique stochastique des ressources en eau, sur la demande en énergie et sur le profil d'irrigation. Tous ces aspects sont affectés à court et à long terme par un large éventail de conditions différentes (hydrologique, météorologique et hydraulique). Au cours de ce projet, une étude bibliographique a été réalisée afin d'identifier les problèmes techniques qui empêchent l'utilisation efficace des centrales hydroélectriques dans les pays en développement. Le système est modélisé numériquement en tenant compte de toutes les variables et paramètres impliqués dans le fonctionnement optimal. L'approche la plus appropriée est choisie afin de maximiser l'utilisation efficace de l'eau et de minimiser les pertes économiques, où différents scénarios sont simulés afin de valider les solutions adoptées. / This research work aims to optimize the operational procedure of cascade hydro plants in order to be efficiently used for power generation and irrigation. The challenge was to find the most realistic model based on the stochastic feature of water resources, on the power demand and on the irrigation profile. All these aspects are affected on the short and on the long run by a wide range of different conditions (hydrological, meteorological and hydraulic). During this project a bibliographic study was done in order to identify the technical issues that prevent the efficient use of hydro plants in developing countries. The system is numerically modelled taking into consideration all the variables and parameters involved in the optimal operation. The most appropriate approach is chosen in order to maximize the efficient use of water and to minimize economical losses, where different scenarios are simulated in order to validate the adopted suggestions.

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.

From the Proceedings of the 1972 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 5-6, 1972, Prescott, Arizona / The use of discrete conditional dependency matrices as input to stochastic decision models is examined. Some of the problems and initial assumptions involved with the construction of the above mentioned matrices are discussed. Covered in considerable detail is the transform used to relate the gamma space with the normal space. A new transform is introduced that should produce reasonable results when the record of streamflow (data) has a highly skewed distribution. Finally, the possibility of using the matrices to provide realistic inputs to a stochastic dynamic program is discussed.

From the Proceedings of the 1978 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 14-15, 1978, Flagstaff, Arizona / The compartmented reservoir is presented as an efficient method of storing water in areas of Arizona having a relatively flat terrain where there is a significant water loss through evaporation. The flat terrain makes it difficult to avoid large surface- area-to-water-volume ratios when using a conventional reservoir. Large water losses through evaporation can be reduced by compartmentalizing shallow impervious reservoirs and in flat terrain concentrating water by pumping it from one compartment to another. Concentrating the water reduces the surface-area-to-water-volume ratio to a minimum, thus decreasing evaporation losses by reducing both the temperature and exposure of the water to the atmosphere. Portable, high-capacity pumps make the method economical for small reservoirs as well as for relatively large reservoirs. Further, the amount of water available for beneficial consumption is usually more than the amount of water pumped for concentration. A Compartmented Reservoir Optimization Program (CROP-76) has been developed for selecting the optimal design configuration. The program has been utilized in designing several systems including several in Arizona. Through the use of the model, the interrelationship of the parameters have been determined. These parameters are volume, area, depth, and slope of the embankment around each compartment. These parameters interface with the parameters describing rainfall and hydrologic characteristics of the watershed. The water -yield model used in CROP-76 requires inputs of watershed area, daily precipitation and daily and maximum depletion. In addition, three sets of seasonal modifying coefficients are required either through calibration or estimated by an experienced hydrologist. The model can determine runoff from two types of watersheds, a natural and /or treated catchment. Additional inputs of CROP-76 are the surface water evaporation rate and the amount and type of consumptive use.

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