River flood forecasting has a significant social and economic impact as it can help in protection from water shortages and possible flood damages. River flood forecasting is very difficult process since it means handling large amounts of dynamic nonlinear systems with a great amount of uncertainty and noisy data. In addition, the data about many variables that affect flood are not available and the underlying physical relationships are not fully understood. A model that combines both neural networks and fuzzy systems can be effective in handling this problem. This system will have the ability to learn from data with good generalization capability using neural networks and to deal effectively with uncertainty using fuzzy systems. ANFIS (Adaptive Network based Fuzzy Inference System) is a model that combines both neural networks and fuzzy systems. In this paper we use ANFIS for forecasting river Nile flood. In addition to ANFIS, we use regression and neural networks for river Nile flood forecasting and then we make a comparison between the performances of the three techniques in forecasting river Nile flood
El-Naggar, A., & Sanhouri, E. (2008). Forecasting Nile River Flood Using a Fuzzy Neural Network Model. The Egyptian Statistical Journal, 52(1), 27-43. doi: 10.21608/esju.2008.234678
MLA
Azza El-Naggar; Eman Sanhouri. "Forecasting Nile River Flood Using a Fuzzy Neural Network Model", The Egyptian Statistical Journal, 52, 1, 2008, 27-43. doi: 10.21608/esju.2008.234678
HARVARD
El-Naggar, A., Sanhouri, E. (2008). 'Forecasting Nile River Flood Using a Fuzzy Neural Network Model', The Egyptian Statistical Journal, 52(1), pp. 27-43. doi: 10.21608/esju.2008.234678
VANCOUVER
El-Naggar, A., Sanhouri, E. Forecasting Nile River Flood Using a Fuzzy Neural Network Model. The Egyptian Statistical Journal, 2008; 52(1): 27-43. doi: 10.21608/esju.2008.234678
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