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The present paper deliberates an important aspect of the data analysis and predictions based on them in the climate environments. The main aspect is how data can arrive in a more efficient way to the central station and how to minimize the transport load in the network. The proposed architecture includes two data collecting ways from different sources. Through a dynamic environment modelling program called Stella, it is created a mobile agent architecture based on a dynamic model of a real environment for Apuseni Mountains area. A set of equations is realized to describe the proposed model. The communication model used, the coordination type between agents and the agents training process are discuss. This article presents also aspects of the intelligent analysis data techniques and phases that are used in order to evaluate data. The obtained results are combined afterwards with the risk factors, which affects the environment through the climatically and meteorological events.