2014/06/18 by Sanjay Chakraborty, N. K. Nagwani, Chakraborty, Sanjay +4
Computer Science · Environmental Science · #Advanced Clustering Algorithms Research #Computers and Society (cs.CY) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Hydrological Forecasting Using AI #cs.CY
paper · pdf · doi:10.48550/arxiv.1406.4756
arxiv created 2014/06/18 · openalex publication_date 2014/06/18 · arxiv updated 2014/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.