A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

Abstract

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques.In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

“…The best ANFIS model is selected using the fuzzy logic toolbox in MATLAB software version 2008 [48], based on a lower RMSE for both the training and the verifying data sets, in which the RMSE is under control and is not increasing [49,50].…”

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

“…ANFIS is an artificial intelligence technique that has been successfully used for mapping input-output relationship based on available data sets (Jang et al, 1997;El-Shafie et al, 2007). It is based on the first order Sugeno-fuzzy inference system proposed by Jang, 1993 and it uses neural network learning algorithms and fuzzy reasoning to map an input space to an output space.…”

Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model

“…Many hybrid models have been proposed as predictors to improve the accuracy of hydrological time-series forecasts, such as the wavelet artificial neural network (ANN) model (Anctil and Tape, 2004;Krishna et al, 2011;Nayak et al, 2013), the periodic ANN (PANN) model (Wang et al, 2006), the chaotic ANN model (Karunasinghe and Liong, 2006), the hybrid fuzzy-ANN model (Nayak et al, 2007), the wavelet-based grey model (Chou, 2007), the wavelet-based NF (neuro-fuzzy) model (Partal and Kisi, 2007;Engin et al, 2007;El-Shafie et al, 2007), the non-supervised ANN-EA (evolutionary algorithms) model (Cao and Park, 2007;Chang et al, 2007), the fuzzy-SVM model (Hua et al, 2008), the wavelet-based multi-layer perceptron model (Kisi, 2008), the wavelet-regression (WR) model (Kisi, 2011), and the wavelet-based fuzzy logic model (Ozger et al, 2012). These hybrid models have shown different advantages for accurate predictions due to their capabilities of utilising present information effectively.…”