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Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach

M. Sudha1 *

1 Department of Information Technology, VIT University, Tamil Nadu India

DOI: http://dx.doi.org/10.12944/CWE.12.2.27

Recently, hybrid data-driven models have become appropriate predictive patterns in various hydrological forecast scenarios. Especially, meteorology has witnessed that there is a need for a much better approach to handle weather-related parameters intelligently.  To handle this challenging issue, this research intends to apply the fuzzy and ANN theories for developing hybridized adaptive rough-neuro-fuzzy intelligent system. . Assimilating the features of ANN and FIS has attracted the rising attention of researchers due to the growing requisite of adaptive intelligent systems to solve the real world requirements. The proposed model is capable of handling soft rule boundaries and linguistic variables to improve the prediction accuracy. The adaptive rough-neuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques.


Rainfall prediction; Data-driven approach; Fuzzy and neural network

Copy the following to cite this article:

M. Sudha. Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach. Curr World Environ 2017;12(2). DOI:http://dx.doi.org/10.12944/CWE.12.2.27

Copy the following to cite this URL:

M. Sudha. Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach. Curr World Environ 2017;12(2). Available from: http://www.cwejournal.org/?p=17348

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