A Multi-Level Fusion Approach for Climate Variation Study using Multi-Source Data — Case Study: Algeria

The study of climate variation strongly depends on data availability and reliability which is collected from multiple sources in different types (multimodal). Up-today It was difficult to have a homogeneous climatic database for Algeria, since some climatic parameters (insolation, temperature, relative humidity and rainfall) are measured over several years and sometimes with data gaps without treatment or combination in many geographical regions. This work proposes a new multi-level data fusion approach to study the climate variation from multi-source and multimodal parameters on spatial and temporal vectors in Algeria. It facilitates the localization of geographical zones with high climate variation potential through fusion of classifier's decision results in high-level stage. A new Linked data model has been developed to manage links from multi-source as well as to build data fusion rules at the low-level stage.