Weather Forecasting using Fuzzy Neural Network (FNN) and Hierarchy Particle Swarm Optimization Algorithm (HPSO)

Background: Weather forecasting has become one of the active research areas due to major scientific and technological problems. An analysis is made to utilize of data mining approaches in weather forecast. Methods: Fuzzy Neural Network (FNN) and Hierarchical Particle Swarm Optimization (HPSO) are used for predicting the weather changes occur in the atmosphere. The FNN uses biological neurons for exact calculation and the neural network adjust their weights by the practice of training. The HPSO is used here for better optimization of weights, also that to improve the overall performance Adaptive Inertia Weight Algorithm (AWA) is proposed. Results: As a result, the total error is reduced with little tolerance which results in accurate weather prediction. The network is trained in such a manner that the model will provide 94% of optimum results. Conclusion: The performances of the proposed algorithm were compared with other existing algorithm using other standard performance metrics which gives best results for the mean weather variables. The results show that the proposed approach is efficient in determining weather forecasting and moreover helps in climate change investigations.