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The physical process of oxygen transfer or oxygen absorption from the atmosphere acts to replenish the used oxygen, a process termed re‐aeration or aeration. Aeration enhancement by macro‐roughness is well‐known in water treatment and one form is the aeration cascade. The macro‐roughness of the steps significantly reduces the flow velocities and leads to flow aeration along the stepped cascade. In this paper, the aeration efficiency in stepped cascade aerators was modeled by using the Adaptive Network Based Fuzzy Inference System (ANFIS). The obtained model was tested with experimental data. Test results showed that ANFIS can be used to estimate the aeration efficiency in stepped cascade aerators.

Chute flow may be either smooth or stepped. The flow conditions in stepped chutes have been classified into nappe, transition and skimming flows. In this paper, characteristics of flow conditions are presented systematically under a wide range of critical flow depth, step height and chute slope. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to predict flow conditions in stepped chutes using critical flow depth, step height and chute slope information. The proposed model performance is determined by threefold cross validation method. The evaluated classification accuracy of ANFIS model is 99.01%. The test results showed that the proposed ANFIS model can be used successfully for complex process control in hydraulic systems.

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