Fuzzy Rule-Based Modeling of Reservoir Operation

Shrestha, Bijaya P.; Duckstein, Lucien; Stakhiv, Eugene Z.

doi:10.1061/(asce)0733-9496(1996)122:4(262)

Cited by 162 publications

(105 citation statements)

References 16 publications

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“…Johnson & Ayyub (1996). Shrestha et al (1996) carried out the fuzzy rule-based control systems for reservoir operation. Kindler (1992) applied fuzzy logic for optimal water allocation.…”

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confidence: 99%

ANFIS approach to the scour depth prediction at a bridge abutment

Muzzammil

2010

Journal of Hydroinformatics

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An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour counter-measure to be implemented against excessive scouring.Despite analysis of innumerable prototype and hydraulic model studies in the past, the scour depth prediction at the bridge abutments has remained inconclusive. This paper presents an alternative to the conventional regression model (RM) in the form of an adaptive network-based fuzzy inference system (ANFIS) modelling. The performance of ANFIS over RM and artificial neural networks (ANNs) is assessed here. It was found that the ANFIS model performed best among of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.

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“…Johnson & Ayyub (1996). Shrestha et al (1996) carried out the fuzzy rule-based control systems for reservoir operation. Kindler (1992) applied fuzzy logic for optimal water allocation.…”

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confidence: 99%

ANFIS approach to the scour depth prediction at a bridge abutment

Muzzammil

2010

Journal of Hydroinformatics

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“…Willis et al (1984) devised a different approach that utilized the probability mass function of the optimal releases, conditioned on reservoir storage and inflow. Other alternatives to regression analysis are the application of artificial neural networks (Farias et al, 2006;Chandramouli & Deka, 2005) and fuzzy rule-based modeling (Shrestha et al, 1996) to inferring the operating rules. Unlike most previous applications of ISO, this study explores its use in a system under semiarid conditions.…”

Section: Implicit Stochastic Optimizationmentioning

confidence: 99%

Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil

Celeste

Curi

2009

Pesqui. Oper.

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This paper deals with the application of Implicit Stochastic Optimization (ISO) to determine monthly operating rules for a reservoir system located in the semiarid Northeast of Brazil. ISO employs a deterministic optimization model to find optimal reservoir allocations under several possible inflow scenarios and later constructs the rules by analyzing the ensemble of these optimal releases. The operating policies provide the monthly reservoir release conditioned on the storage at the beginning of the month and the inflow predicted for the month. In addition to the classical regression analysis, this study establishes the rules by a two-dimensional interpolation strategy. After the rules are identified, they are applied to operate the system under new inflow realizations and show ability to produce policies similar to those obtained by deterministic optimization taking the same inflows as perfect forecasts.Keywords: reservoir operation; implicit stochastic optimization; semiarid regions. ResumoEste artigo investiga a aplicação de Otimização Estocástica Implícita (OEI) para determinar regras de operações mensais em um sistema de reservatórios localizado no nordeste semi-árido brasileiro. OEI emprega um modelo de otimização determinística para encontrar alocações ótimas do reservatório sob vários cenários possíveis de afluências e posteriormente constrói as regras a partir da análise deste conjunto de liberações ótimas. As políticas operacionais fornecem a alocação mensal do reservatório condicionada ao armazenamento no início do mês e a afluência prevista para o mês. Além da clássica análise de regressão, este estudo estabelece as regras por meio de uma estratégia de interpolação bidimensional. Após a sua identificação, as regras são aplicadas para operar o sistema sob novas realizações de afluências e mostram habilidade para produzir políticas semelhantes às obtidas a partir de otimização determinística tendo estas mesmas afluências como previsão perfeita.Palavras-chave: operação de reservatórios; otimização estocástica implícita; semi-árido.Celeste, Curi & Curi -Implicit stochastic optimization for deriving reservoir operating rules in semiarid Brazil

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“…Faye et al (2003) presented a fuzzy logic model for long-term storage/transfer/distribution of water management system. One other class of problems involves systems of fuzzy rules which have applications in: reservoir management where Shrestha et al (1996) worked on a multipurpose reservoir system, hydrology problems where Piechota and Dracup (1996) used 94 years of monthly Palmer Drought Severity Index (PDSI) data and their article investigates the hydroclimatic response in the United States to the extreme phases of the Southern Oscillation (El Niño and La Nina). Dou et al (1999) developed a fuzzy rule-based model for solute transport in the unsaturated zone.…”

Section: Introductionmentioning

confidence: 99%

Soil water management problems using fuzzy arithmetic

2013

Global NEST Journal

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Drainage management problems are usually very hard to simulate due to the uncertainty of the hydraulic parameters involved. Fuzzy analysis is one of the available tools that can be used for such problems, involving uncertain data. A fuzzy analysis approach usually involves the consideration of several α-level cuts and an analytical approach or an explicit scheme approach for the PDE's discretization. Several application examples of this approach are listed in the literature, including uncertainty in hydraulic conductivity, specific yield, transmissivities, porosities, dispersivities, and deoxygenation rate coefficient. A methodology for the simulation of drainage problem having vague values of hydraulic parameters is introduced in this paper, and an analytical solution for a two-dimensional drainage application is presented. The two-dimensional problem of drainage is handled using fuzzy analysis by defining the hydraulic conductivity K as a triangular fuzzy number (TFN). The method of interval analysis is used in all the α-level cut examples. A solution is obtained using eleven α-level cuts and also solutions for two, three, and five α-level cuts are presented. Trials for different values of effective porosity are also performed. Finally conclusions on the necessary number of α-cuts utilized for drainage problems are drawn.

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Fuzzy Rule-Based Modeling of Reservoir Operation

Cited by 162 publications

References 16 publications

ANFIS approach to the scour depth prediction at a bridge abutment

ANFIS approach to the scour depth prediction at a bridge abutment

Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil

Soil water management problems using fuzzy arithmetic

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