Abstract
AbstractClimate on Earth is chaotic; hence, the meteorological department faces though challenge of forecasting weather to maximum accuracy. Weather forecasting is very important as it is the first step towards the preparation against the upcoming natural hazard and hence causing threat to life form. The objective of the paper is to study weather parameters using different clustering and classification techniques and select the best-suited model on the basis of least error and less time taking. Weather parameters such as max. and min. temperature, wind Speed, rainfall, relative humidity, evaporation, bright sun shine hours, average temperature, average humidity from 1 January 2017 till 30 September 2018 of Delhi region are studied. Navie Bayes provides better results as compared to others on the basis of statistical outcomes of Kappa Statistics, MAE, RMSE, RAE, RRSE. Clustering is the technique used for clustering the weather parameters into groups having similarity, and classification refers to the forecasting of weather parameters on the basis of the input data by training, validating, and testing the data set and further forecasting. K-means clustering, EM clustering, hierarchical clustered are the clustering methods used for study, and on the biases of the less time taken, it is concluded that K-means clustering is efficient clustering technique.KeywordsClassificationClusteringNavie BayesJ48LivSVMK-means clusteringEM clusteringHierarchical clusteredData mining
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.