A Framework for Extracting Meteorological Data with MapReduce and Predictive Modeling of Floods Using Machine Learning

This article introduces an innovative approach that integrates machine learning with the MapReduce algorithm to efficiently extract and analyze meteorological data. The proposed methodology combines these elements to improve the prediction of extreme weather events while reducing system execution time. Evaluations conducted through simulations reveal a significant enhancement in prediction accuracy compared to traditional methods. The findings of this research present new opportunities for application in fields such as agriculture and urban planning. However, certain limitations have been identified, including the narrow geographical scope of the analysis and the quality of the meteorological data used. To strengthen this approach, future research should focus on extending its application to other geographic areas and improving the quality of the meteorological datasets used.