Predicting water levels in the Rhine river using Temporal Convolutional Networks
| dc.contributor.advisor | gucluturk, y | |
| dc.contributor.author | Dijkstra, Rick | |
| dc.date.issued | 2019-07-26 | |
| dc.description.abstract | To ensure that the people in The Netherlands keep dry feet, Rijkswaterstaat (a Dutch gov- ernmental institute) wants to know whether deep learning models can be a valuable addition to current water level prediction models. For this, we set as a reference point the multilinear regression model LobithW, which Rijkswaterstaat uses to predict water levels up to two days ahead in Lobith. We developed LobithAI, using Temporal Convolutional Networks. Due to missing data, it was impossible to have a direct comparison between the two models. In- stead, we used two di erent evaluation strategies. When compared on data in the same time range, LobithAI outperforms LobithW on both RMSE and precision. When compared with LobithW directly after its validation, LobithAI scores higher on the RMSE, but LobithW has higher precision for predictions one day ahead. Extensions to LobithAI are also possible, with the best performing extension being the increase of the number of predictions per day. We also inspected LobithAI using activation adjustment, showing that the model mostly uses the closest and most recent measurements. While the comparison between the models was incomplete, the results from the evaluations and the exibility of LobithAI make it a valuable addition to the models of Rijkswaterstaat. | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/10693 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Master Artificial Intelligence | en_US |
| dc.thesis.studyprogramme | Artificial Intelligence | en_US |
| dc.thesis.type | Master | en_US |
| dc.title | Predicting water levels in the Rhine river using Temporal Convolutional Networks | en_US |
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