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The primary purpose of a weir is to measure discharge. Moreover, it can be used as a tool to increase aeration efficiency in rivers, fish hatcheries, and water treatment plants. A free overfall jet from a weir plunging into downstream water causes entrainment of the air bubbles if the free overfall jet velocity exceeds a certain critical value and hence aeration occurs. In recent years, different soft computing systems have been successfully employed for the solution of complex problems. The aim of this study is to develop models to estimate air entrainment and aeration efficiencies of weirs using soft computing systems. For this aim, genetic expression programming, a recently developed artificial intelligence technique, is used. Genetic Expression Programming (GEP) is a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. GEP is preferred since it generates a mathematical function which fits to given experimental data. The results indicate that there are good agreements between the measured values and the values obtained by the genetic expression programming models. These good agreements confirm the validity of the developed genetic expression programming models. [ABSTRACT FROM AUTHOR]

Stepped chutes that are commonly used for gabion weirs, river training, irrigation channels, and storm waterways have become popular in recent years mainly due to the intrinsic low-cost and the speed of construction. Stepped chutes are used also for in-stream re-aeration and in water treatment plants to enhance the air-water transfer of atmospheric gases and of volatile organic components. In a stepped chute, the provision of steps can produce significant energy dissipation. This paper seeks the effect of energy dissipation over stepped chutes on aeration efficiency. It is observed from the results that aeration efficiency increases with increasing energyloss ratio. There is higher energy dissipation in nappe flows than skimming flow situations and nappe flow regime lead to greater aeration efficiency than skimming flow regime. Moreover, regression equations are obtained relating aeration efficiency to stepped chute slope and energy-loss ratio. For this aim, genetic expression programming, a recently developed artificial intelligence technique, is used. The model equations obtained have high correlation coefficients and low minimum square error values. [ABSTRACT FROM AUTHOR]