Abstract
Weather forecasting is carried out using different external measuring devices of weather predictions based on previous data. These devices or techniques obtain an obvious report of the prediction of weather. In our perspective, research is performed through many previous datasets in the weather forecast. Incorporating a model of multilinear regression and quantile regression allows visualizing the previous data to analyze the weather prediction. The dataset of precipitation levels is trained as a multilinear regression and gives the final level of estimation between rainfall in 2019 and rainfall in 2020. An analysis of a dependent variable and independent variables is multilinear regression. Through this study of approach algorithms, the seasons can be distinguished depending on the visualization.
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Bano, S., Niharika, G.L., Deepika, T., Nishitha, S.N.T., Pranathi, Y.L. (2022). Weather Divergence of Season Through Regression Analytics. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_10
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DOI: https://doi.org/10.1007/978-981-16-2422-3_10
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