The Rift Valley fever (RVF), which first appeared in Kenya in 1912, is an anthropozoonosis widespread in tropical areas. In Senegal, it is particularly felt in the Ferlo area where a strong presence of ponds shared by humans, cattle and vectors is noted. As part of the studies carried out on the environmental factors which favour its start and propagation, the focus of this paper is put on the decision making process to evaluate the impacts, the interactions and to make RVF monitoring easier. The present paper proposes a model based on data mining techniques and dedicated to trade experts. This model integrates all the involved data and the results of the analyses made on the characteristics of the surrounding ponds. This approach presents some advantage in revealing the relationship between environmental factors and RVF transmission vectors for space-time epidemiology monitoring purpose.