dortmund.de

dortmund.de Many applications that aim at enhancing sustainability rely on some sort of spatio-temporal model. The task can be monitoring or prediction in traffic networks, power grids, building energy management, river flow volume, and sea level – to mention just a few. The positive effect on the en-vironment is achieved by a better control, better planning of processes or better disaster management. Spatio-temporal models predict some states of variables over time that are spatially ordered in some topology. They can be used in a variety of applications, ranging from energy-saving techno-logical administration to monitoring for early alarms or to supporting better emergency plans. Graphical Models have been successfully used for predictions based on structured data. Here, we introduce a functional form for the model parameters that allows a spatio-temporal predictive analy-sis with continuous time. Though intended also to be used in engineering approaches, there, we are missing public data sets. Hence, the use of our model is demonstrated on two exemplary applications, namely the prediction of sea-levels for tsunami prediction and the prediction of traffic jam as a consequence of a flow of refugees in case of a nuclear acci-dent. Categories and Subject Descriptors V.2 [Transportation]; H.3 [Mining massive through