Using Artificial Neural Networks to Model Running Speed in Orienteering
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This thesis concerns the modeling of running speed in orient-eering by means of multi-layered feed forward artificial neu-ral networks with Backpropagation learning, using GPS tracks of orienteers, an orienteering map and a digital eleva-tion model as the basis for the training data. A learning sys-tem was implemented and tested with GPS data collected by a test subject. A trained speed model was applied in a third-party application for arithmetic analysis of route choices. The proposed method is shown to have potential, even though the results as of now are not good enough to be considered useful to orienteers.