An Optimal K-Nearest Neighbor for Weather Prediction Using Whale Optimization Algorithm

Rajalakshmi Shenbaga Moorthy, Pabitha Parameshwaran

International Journal of Applied Metaheuristic Computing · 29 October 2021

In one sentence. Whale Optimization tuning of k-nearest neighbours for weather prediction, reporting better accuracy and lower run time than plain K-NN on an unspecified dataset.

Abstract

The weather has a serious impact on the environment as it affects to change day to day life. In recent days, many algorithms were proposed to predict the weather. Although various machine learning algorithms predict the weather, the optimal prediction of weather is not addressed. Optimal Prediction of weather is required as it has a serious impact on human life. Thus this domain invites an optimal system that can forecast weather thereby saving human life. To optimally predict the changes in weather, a metaheuristic algorithm called Whale Optimization Algorithm (WOA) is integrated with machine learning algorithm K- Nearest Neighbor (K-NN). Whale optimization is an algorithm inspired by the social behavior of whales. The proposed WOAK-NN is compared with K-NN. The integration of WOA with K-NN aims to maximize accuracy, F-measure and minimize mean absolute error. Also, the time complexity of WOAK-NN is compared with K-NN and observed that when the dataset is large, WOAK-NN requires minimum time for an optimal prediction.

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