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 in time-series forecasting leading to satisfactory performances. A further validation of the ANFIS model has been done by applying it to another river basin, Tar River Basin in USA. The results evaluated on the basis of standard statistical criteria showed improved performances by the ANFIS depth-depth forecasting models. The results also indicate that performances of the ANFIS models with multiple inflows are more satisfactory and closely follow performances of the ARIMA models. The study demonstrates applications of the multiple inflows ANFIS models in forecasting downstream flood flow and flow depth in a river system.
This study presents a weighted pre-emptive goal programming model formulation for coordinated reservoir operation, with easy inclusion of uncontrolled water flows. The model is combined with a multiple water inflows forecasting model, and can be used for real time reservoir operation. Water flow routing from various upstream sites is accounted by with a single compact equation. Integration of controlled and uncontrolled water flows in the optimization model simplifies the operation model, resulting in accurate computation of the downstream water flow. Multiple objectives with water storage and flow variables are used to derive optimal regulation for a reservoir system under flood conditions. For real time operations, the model can be used to determine optimal water release rates for a current period, on the basis of an optimal water release schedule for an operating horizon (T). The model is applied to the flood control operation of reservoirs in the Narmada River Basin (India), with three controlled and three uncontrolled water flows affecting the downstream flow at Hoshangabad. Reservoir water storage and downstream control point flows are zoned, with prioritized objectives used to derive the optimal water release rates. Model applications to the 1999 flood event in the Narmada River Basin with observed and forecasted inflows illustrates that, if water inflows were known through a forecasting technique well in advance, the coordinated operation of the reservoirs could substantially reduce the peak water flows at the control points. The study also indicates that uncontrolled channel flows at the damage site were sufficiently high to cause flooding at the damage site.
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