Forecasting and building operational indicators for sugarcane production through time series
Abstract
Climate studies have gained relevance due to the increase in climatic events with severe impacts observed in the last decade, especially in urban areas. For example, large volumes of precipitation can cause floods and landslides, impacting city traffic and even costing citizens' lives. In order to make it possible to monitor rainfall volumes, pluviometers are installed throughout the country. However, such stations are controlled by multiple organizations and produce data in different space/time resolutions and formats. This paper proposes TEMPO, a system that uses OLAP (Online Analytical Processing) techniques to propose efficient storage, query, and analysis mechanisms to handle pluviometers data. To evaluate the tool, we present a case study showing the integration and analysis of data from CEMADEN and Alerta Rio.
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