Prediction of flow through rockfill dams using a neuro-fuzzy computing technique
作者： Majid Heydari , Parisa Hosseinzadeh Talaee
关键词: Neuro-fuzzy 、 Finite volume method 、 Geotechnical engineering 、 Fuzzy model 、 Flow (mathematics) 、 Finite element method 、 Inference system 、 Engineering 、 Numerical analysis 、 Membership function
摘要: Rockfill dams are economical and fast tools for flood detention control purposes. Artificial intelligence approaches may provide user-friendly alternatives to very complex time-consuming numerical methods such as finite volume element predicting flow through rockfill dam. Therefore, this paper examines the potential of coactive neuro-fuzzy inference system (CANFIS) estimation trapezoidal rectangular dams. The results showed that accurate predictions can be achieved with a CANFIS Takagi–Sugeno–Kang (TSK) fuzzy model Bell membership function both Furthermore, LevenbergMarquardt Delta-Bar-Delta were best algorithms training network in order estimate dams, respectively. Overall, study suggest possibility using prediction
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