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

In rough set, data analysis starts from a table referred to as decision or information table representing an information system30. A wide range of scientific and medical applications, especially in the field of pattern recognition, data mining, machine learning and process control systems adopted the rough set as a suitable tool29. Zadeh introduced Fuzzy set theory to the researchers and mathematicians in 1967 stating that, it is not required to have a precise, numerical information input for modeling a system40. This research applies Fuzzy inference system that maps a given input to output using the fuzzy sets theory that uses Sugeno method24. The artificial ANN Abstract 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 roughneuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques. Article history