Book contents
- Frontmatter
- Contents
- List of Contributors
- 1 Introduction
- 2 Integrated regional risk assessment and safety management: Challenge from Agenda 21
- 3 Risk analysis: The unbearable cleverness of bluffing
- 4 Aspects of uncertainty, reliability, and risk in flood forecasting systems incorporating weather radar
- 5 Probabilistic hydrometeorological forecasting
- 6 Flood risk management: Risk cartography for objective negotiations
- 7 Responses to the variability and increasing uncertainty of climate in Australia
- 8 Developing an indicator of a community's disaster risk awareness
- 9 Determination of capture zones of wells by Monte Carlo simulation
- 10 Controlling three levels of uncertainties for ecological risk models
- 11 Stochastic precipitation-runoff modeling for water yield from a semi-arid forested watershed
- 12 Regional assessment of the impact of climate change on the yield of water supply systems
- 13 Hydrological risk under nonstationary conditions changing hydroclimatological input
- 14 Fuzzy compromise approach to water resources systems planning under uncertainty
- 15 System and component uncertainties in water resources
- 16 Managing water quality under uncertainty: Application of a new stochastic branch and bound method
- 17 Uncertainty in risk analysis of water resources systems under climate change
- 18 Risk and reliability in water resources management: Theory and practice
- 19 Quantifying system sustainability using multiple risk criteria
- 20 Irreversibility and sustainability in water resources systems
- 21 Future of reservoirs and their management criteria
- 22 Performance criteria for multiunit reservoir operation and water allocation problems
- 23 Risk management for hydraulic systems under hydrological loads
22 - Performance criteria for multiunit reservoir operation and water allocation problems
Published online by Cambridge University Press: 18 January 2010
- Frontmatter
- Contents
- List of Contributors
- 1 Introduction
- 2 Integrated regional risk assessment and safety management: Challenge from Agenda 21
- 3 Risk analysis: The unbearable cleverness of bluffing
- 4 Aspects of uncertainty, reliability, and risk in flood forecasting systems incorporating weather radar
- 5 Probabilistic hydrometeorological forecasting
- 6 Flood risk management: Risk cartography for objective negotiations
- 7 Responses to the variability and increasing uncertainty of climate in Australia
- 8 Developing an indicator of a community's disaster risk awareness
- 9 Determination of capture zones of wells by Monte Carlo simulation
- 10 Controlling three levels of uncertainties for ecological risk models
- 11 Stochastic precipitation-runoff modeling for water yield from a semi-arid forested watershed
- 12 Regional assessment of the impact of climate change on the yield of water supply systems
- 13 Hydrological risk under nonstationary conditions changing hydroclimatological input
- 14 Fuzzy compromise approach to water resources systems planning under uncertainty
- 15 System and component uncertainties in water resources
- 16 Managing water quality under uncertainty: Application of a new stochastic branch and bound method
- 17 Uncertainty in risk analysis of water resources systems under climate change
- 18 Risk and reliability in water resources management: Theory and practice
- 19 Quantifying system sustainability using multiple risk criteria
- 20 Irreversibility and sustainability in water resources systems
- 21 Future of reservoirs and their management criteria
- 22 Performance criteria for multiunit reservoir operation and water allocation problems
- 23 Risk management for hydraulic systems under hydrological loads
Summary
ABSTRACT
A genetic algorithm model was developed to derive the best water allocation distribution within a multiple-reservoir water supply system. Three different objective functions were used to test the applicability of the model on a real-world seven-reservoir system. The appraisal of obtained solutions was carried out through the respective system's performance evaluation using a number of performance indicators. Due to the difference in the objective functions, the use of performance indicators proved crucial in the comparison of the solutions proposed by the three models. In addition, in all of the three cases the resulting release distributions produced in repeated runs of the same model showed substantial variability. The variability, however, was not reflected in the respective objective function achievement, indicating that there might be a number of potential solutions to the problem. In this respect, the comparison of the related performance indicator estimates was found to be a valuable means to provide a better insight into the essential difference between different solutions.
INTRODUCTION
Genetic algorithms (GA) fall into a group of search strategies that are based on the Darwinian concept of biological evolution. They apply the principles of natural genetics and selection to solve optimization problems related to artificial systems (Holland 1975). By using the objective function as a fitness measure, GAs emulate the Darwinian concept of “survival of the fittest” on a population of artificial beings to search the solution space of the optimization problem. The artificial “creatures” that the search is based on represent a specific coding of potential solutions to the problem.
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- Publisher: Cambridge University PressPrint publication year: 2002
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