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

M Sudha

doi:10.12944/cwe.12.2.27

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 rough-neuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques.

Highlights

In rough set, data analysis starts from a table referred to as decision or information table representing an information system[30]
ANNs and fuzzy logic approach applied for forecasting weather in different areas of china reported that the prediction accuracy achieved by the proposed models was satisfactory than other existing methods[4,5] Applied adaptive neuro fuzzy inference system (ANFIS) for forecasting drought, the model reported improved for forecast accuracy[6]
ANFIS maps the input members to an intended input membership function and input MF to a set of if- rules.The derived output rule set characteristics are mapped to output memberships, and the output MFs are converted to single valued decision associated with the output[12]

Summary

Introduction

Data analysis starts from a table referred to as decision or information table representing an information system[30]. 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 tool[29]. This research applies Fuzzy inference system that maps a given input to output using the fuzzy sets theory that uses Sugeno method[24].The artificial ANN (ANN) model was developed by Rosenblatt in 195831. As a recent trend adaptive neuro fuzzy inference system (ANFIS) is widely used for modeling daily rainfall prediction[18]. ANNs and fuzzy logic approach applied for forecasting weather in different areas of china reported that the prediction accuracy achieved by the proposed models was satisfactory than other existing methods[4,5] Applied ANFIS for forecasting drought, the model reported improved for forecast accuracy[6]

Methods

Results

Conclusion