The topic of critical hydrogeological phenomena, due to flooding, has a particular relevance given the risk that it implies. In this paper we simulated complex weather scenarios in which forecasts coming from different sources become relevant. Our basic idea is that agents can build their own evaluations on the future weather events integrating these different information sources also considering how trustworthy each single source is with respect to each individual agent. These agents learn the sources' trustworthiness in a training phase. Moreover, agents are differentiated on the basis of their own ability to make direct weather forecasts, on their possibility to receive bad or good forecasts from the authority, and on the possibility of being influenced by the neighbors' behaviors. Quite often in the real scenarios some irrational behaviors rise up, whereby individuals tend to impulsively follow the crowd, regardless of its reliability. To model that, we introduced an impulsivity factor that measures how agents are influenced by the neighbors' behavior, a sort of "crowd effect". The results of these simulations show that, thanks to a proper trust evaluation of their sources made in the training phase, the different kinds of agents are able to better identify the future events.
