Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Sy...

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Vydáno v:	Water resources management Ročník 27; číslo 9; s. 3507 - 3523
Hlavní autoři:	Akrami, Seyed Ahmad, El-Shafie, Ahmed, Jaafar, Othman
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.07.2013 Springer Springer Nature B.V
Témata:	Adaptive systems Algorithms Approximation Artificial neural networks Atmospheric Sciences Civil Engineering correlation Correlation coefficient Data analysis Earth and Environmental Science Earth Sciences Earth, ocean, space Efficiency Environment Errors Exact sciences and technology Floods Forecasting Forecasting techniques Fuzzy logic Fuzzy sets Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology Hydrology. Hydrogeology Hydrology/Water Resources Inference Mathematical models model validation Neural networks prediction Rain Rainfall runoff Studies Water management Water resources Water resources management Fuzzy rules Modified ANFIS Fitting parameters Adaptive neuro-fuzzy Inference systems (ANFIS) Converges of iterations Rainfall prediction models efficiency rainfall neural networks runoff accuracy rain water surface water noise water resource management prediction correlation coefficient errors
ISSN:	0920-4741, 1573-1650
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