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- W138547447 abstract "In water resource management, efficient controllers of stormwater tanks prevent flooding of sewage systems, which reduces environmental pollution. With accurate predictions of stormwater tank fill levels based on past rainfall, such controlling systems are able to detect state changes as early as possible. Up to now, good results on this problem could only be achieved by applying special-purpose models especially designed for stormwater prediction. The question arises whether it is possible to replace such specialized models with state-of-the-art machine learning methods, such as Support Vector Machines (SVM) in combination with consequent parameter tuning using sequential parameter optimization, to achieve competitive performance. This study shows that even superior results can be obtained if the SVM hyperparameters and the considered preprocessing is tuned. Unfortunately, this tuning might also result in overfitting or oversearching – both effects would lead to declined model generalizability. We analyze our tuned models and present possibilities to circumvent the effects of overfitting and oversearching." @default.
- W138547447 created "2016-06-24" @default.
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- W138547447 date "2010-01-01" @default.
- W138547447 modified "2023-09-27" @default.
- W138547447 title "Optimizing Support Vector Machines for Stormwater Prediction" @default.
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