This paper deals with a comparison of two differ- ent fault diagnosis frameworks. The first method is based on a temporal/spatial model-based analysis by exploiting a-priori information about the system under study, so fault detection is based on monitoring the residuals of combined spatial and time series models obtained from the network. The second method aims at characterizing and detecting changes in the probabilistic pattern sequence of data coming from the network. Relation- ships between data streams are modelled through sequences of linear dynamic time-invariant models whose trained coefficients are used to feed a Hidden Markov Model (HMM). When the pattern structure of incoming data cannot be explained by the trained HMM, a change is detected. Here, the performance obtained from this two distinct approaches is examined by using a dataset coming from the Barcelona water transport network.