Efficient data pruning using optimal KNN for weather forecasting in cloud computing. (8th May 2023)

• Record Type: Journal Article
• Authors: Benil, T.; Kumar, P. Krishna; Bharathi, R.
• Is Part Of: International journal of global warming. Volume 30:Number 2(2023)
• Page Start: 137
• Page End: 151
• Publication Date: 2023-05-08
• Subjects: K-nearest neighbour -- numerical weather prediction -- NWP -- cloud computing -- weather forecasting; Global warming -- Periodicals; Global warming -- Statistics -- Periodicals; Global warming -- Economic aspects -- Periodicals; Climatic changes -- Periodicals; Climatic changes -- Economic aspects -- Periodicals; Climatic changes -- Environmental aspects -- Periodicals; 363.73874
• Languages: English
• ISSNs: 1758-2083

Abstract

Established researchers may use cloud-deployed software, information sharing, collaborative effort devices, and cloud-based computer infrastructure to handle data and recreate models. This study examines cloud nomenclature in the meteorological local region to aid numerical weather forecast. Numerical weather prediction forecasts weather. Numerous machine-learning methods are suggested for numerical weather prediction equation evaluation (NWP). Despite these inaccuracies, the methodologies so far provide decent results. It is hard to manage weather history, but it is best to do so. Thus, we propose a novel k-nearest neighbour classifier to prune missing values. Despite these inaccuracies, the methodologies so far provide decent results. The new KNN, optimum k-nearest neighbour, uses an enhanced way to choose out the precise data required for prediction. Seattle rainfall datasets assess performance. We found that the optimum KNN method improves classification accuracy while reducing time.