Flood Flow Modeling in a River System Using Adaptive Neuro-Fuzzy Inference System

This paper presents the application of a data-driven model, Adaptive Neuro-Fuzzy Inference System (ANFIS) in forecasting flood flow in a river system. ANFIS uses neural network algorithms and fuzzy reasoning to map an input space to an output space. In the present study, ANFIS models are used to forecast common downstream flow rates and flow depths in a river system having multiple inflows. Three different ANFIS model forms: (i) depth-depth (H-H) model, (ii) depth-discharge (H-Q) model and (iii) discharge-discharge (Q-Q) models are considered in this study. The models are used for forecasting one-hour ahead common downstream flow rates and flow depths in a river system based on past upstream flows. The flow and flow depths data are divided arbitrarily into different categories (2, 3, 4, 6) and different number of membership functions (Triangular, Gaussian, Trapezoidal and Bell) selecting two categories with Gaussian input and constant output membership functions based on trial and error. Performances of the ANFIS model with selected categories and membership functions are tested and verified by applying a time-series model, Autoregressive Integrated Moving Average (ARIMA) to the same river system. ARIMA has been successfully used i