Many factors influence water quality within a reservoir. Deforestation, excessive erosion, introduction of new species, domestic and industrial waste disposal and agricultural runoff are only a few examples.
It is well known by specialists in water resources management that water levels in a reservoir may also affect its quality. But how these processes occur and how appropriate water levels can be maintained are very hard questions to answer. This is because of the physical and biological processes occurring inside the water body, and also due to the various demands from society concerning water uses.
Nowadays, through the use of models, knowledge of some of the conditions can enable us to predict future conditions. In many cases, reservoir models, such as physical models for water quality, may predict the future water quality situation. These models have been used successfully to enhance knowledge about the interactions among the different parts inherent to the water systems.
Through the combination of water quality and optimization models, this study proposes a suitable methodology for the assessment of planning operations of a storage reservoir. The purpose of this paper is to consider a multipurpose reservoir, under different water demands and uses from societies, concerning reservoir water quality.
The proposed optimization is realized through the use of dynamic programming combined with stochastic techniques that can handle the probabilistic characteristics of inflow quantity and quality. For the water quality assessment, the UNEP/ILEC one-dimensional model with two layers called PAMOLARE is applied. Finally, sensitivity analysis is carried out using a genetic algorithm model. Copyright © 2003 John Wiley & Sons, Ltd.
[1]
Fereidoun Mobasheri,et al.
a Stochastic Dynamic Programming Model for the Optimum Operation of a Multi-Purpose Reservoir
,
1973
.
[2]
M. Palmer Terrell,et al.
CCGP Model for Multiobjective Reservoir Systems
,
1989
.
[3]
Timothy K. Gates,et al.
Planning Reservoir Operations with Imprecise Objectives
,
1997
.
[4]
John W. Labadie.
Combining Simulation and Optimization in River Basin Management
,
1993
.
[5]
J. Dombi.
Membership function as an evaluation
,
1990
.
[6]
Lucien Duckstein,et al.
Fuzzy Rule-Based Modeling of Reservoir Operation
,
1996
.
[7]
V. T. Chow,et al.
Stochastic state variable dynamic programing for reservoir systems analysis
,
1981
.
[8]
S. Yakowitz.
Dynamic programming applications in water resources
,
1982
.
[9]
Keith W. Hipel,et al.
A fuzzy multicriteria model for comparing energy projects
,
1987
.
[10]
S. Montenegro,et al.
Caracterização da Variabilidade Espacial de Parâmetros Hidráulicos em Solos Aluviais no Estado de Pernambuco.UCO
,
1999
.
[11]
David Manuel Lelinho da Motta Marques,et al.
Bases Para a Estruturação de Indicadores de Qualidade de Águas.
,
2000
.
[12]
I. Turksen,et al.
A model for the measurement of membership and the consequences of its empirical implementation
,
1984
.
[13]
R. Harboe.
Explicit Stochastic Optimization
,
1993
.
[14]
Samuel O. Russell,et al.
Reservoir Operating Rules with Fuzzy Programming
,
1996
.