Abstract
Weather event prediction offerings suitable from the obsolete occurrences as a main gigantic obligation, since it depends on upon dissimilar constraints to forecast the destitute factors like air temperature, humidity, precipitation, wind speed, and dampness, which are fluctuating intermittently. A multi-model data mining approach is a unique process for merging the prognostic capability of multiple prototypes for better forecasting accuracy. In this paper, we proposed multi-model ensemble for forecasting weather events. The data mining algorithms Random forest, C5.0, AdaBoost, and Support Vector Machine (SVM) models are implemented in combination as ensemble. The combinations of (RF + SVM + AdaBoost) perform better accuracy with 82.73% in compare with other combinations of multi-model ensembles. For experimental work we used, weather data of Barajas Airport, Madrid, between 1997 and 2015 were gathered from web https://www.wunderground.com/ The Weather Company, LLC.
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Jakhar, Y.K., Mishra, N., Poonia, R. (2021). Weather Event Prediction Using Combination of Data Mining Algorithms. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_33
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DOI: https://doi.org/10.1007/978-981-15-5421-6_33
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