Using Adaptive Neuro-Fuzzy Inference System (ANFIS) to Improve the Long-term Rainfall Forecasting

Authors

  • Nazim Osman Bushara
  • Ajith Abraham

Keywords:

long-term weather forecasting, Rainfall prediction, hybrid intelligent system, neuro-fuzzy system, ANFIS

Abstract

A hybrid intelligent system is one that combines at least two intelligent technologies, for example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. In This study we propose an Adaptive Neuro-Fuzzy Inference System (ANFIS) to develop long-term weather forecasting model for rainfall prediction. Monthly meteorological data that obtained from Central Bureau of Statistics Sudan from 2000 to 2012, for 24 meteorological stations distributed among the country has been used. In the experiments we built several ANFIS models using different types of membership functions, different optimization methods and different dataset ratios for training and testing. The proposed models have been evaluated and compared by using correlation coefficient, mean absolute error and root mean-squared error as performance metrics. The results show that ANFIS neuro-fuzzy model is able to capture the dynamic behavior of the rainfall data and it produced satisfactory results, so it may be useful in long term rainfall prediction.

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Published

2015-01-01

How to Cite

Nazim Osman Bushara, & Ajith Abraham. (2015). Using Adaptive Neuro-Fuzzy Inference System (ANFIS) to Improve the Long-term Rainfall Forecasting. Journal of Network and Innovative Computing, 3, 13. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/98

Issue

Vol. 3 (2015)

Section

Original Article

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