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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Bibliographic Details
Published in:Water resources management Vol. 27; no. 9; pp. 3507 - 3523
Main Authors: Akrami, Seyed Ahmad, El-Shafie, Ahmed, Jaafar, Othman
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.07.2013
Springer
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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 Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient ( R 2 ), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-013-0361-9