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A Dynamic Classification System to Study Climate Variation from Multi-Source Parameters – Case Study: Algeria Insolation

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The study of climate variability strongly depends on data availability and reliability, because it is very difficult to have a homogeneous database in a large geographical region like Algeria. For some meteorological stations, the climatic data of some parameters (insolation, temperature, water vapor pressure, relative humidity and rainfall) are measured over several years, and sometimes with important data gaps. This work proposes a new dynamical approach in order to study climate variation from insolation parameter on spatial and temporal vectors in Algeria. It facilitates the localization of geographical zones with high climate variation potential through classification algorithms. A new model has been developed to manage data links from multi-source as well as to build locally a global climatic database.
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Dynamic Classification; Linked-Data; Decision Making; Climate Variation; Solar Potential; Insolation Map; Algeria

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Algerian ministry of energy and mines,

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