Abstract
This paper presents an overview of the key points that help to build a system for detecting and analyzing afterwards the climate change in the Maghreb region. Our main goal is to propose a design and an implementation of a meteorology data warehouse built from multiple sources and intended to end-users for making prediction and effective decision. More precisely, a digital system involving a clean, complete and consistent store of all gathered data is developed. Such repository is accompanied of data warehousing and mining tools enabling improvement of climate prediction, statistical analysis, climatic classification and clustering, frequent pattern extraction and association rule mining. Unlike previous efforts, the most important purpose of this study is to observe the climate change in Maghreb region and for the long-term the ability to predict natural disasters such as severe floods and droughts.
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Drias, Y., Drias, H., Khennak, I. (2022). Data Warehousing and Mining for Climate Change: Application to the Maghreb Region. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_27
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DOI: https://doi.org/10.1007/978-3-030-96302-6_27
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