Weather Forecasting using Incremental K-means Clustering

TL;DR

This paper presents a novel weather forecasting method using incremental K-means clustering on air pollution data from West Bengal, enabling real-time prediction and pollution impact mitigation.

Contribution

It introduces an incremental K-means clustering approach for weather prediction based on air pollution data, providing a dynamic and adaptive forecasting methodology.

Findings

Effective clustering of air pollution data for weather categorization

Improved real-time weather prediction accuracy

Potential for pollution impact mitigation

Abstract

Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day to day applications.Weather forecasting of this paper is done based on the incremental air pollution database of west Bengal in the years of 2009 and 2010. This paper generally uses typical K-means clustering on the main air pollution database and a list of weather category will be developed based on the maximum mean values of the clusters.Now when the new data are coming, the incremental K-means is used to group those data into those clusters whose weather category has been already defined. Thus it builds up a strategy to predict the weather of the upcoming data of the upcoming days. This forecasting database is totally based on the weather of west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.