@article{AyzelHeistermann2021,
  author    = {Ayzel, Georgy and Heistermann, Maik},
  title     = {The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU},
  series = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  volume    = {149},
  journal   = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0098-3004},
  doi       = {10.1016/j.cageo.2021.104708},
  pages     = {12},
  year      = {2021},
  abstract  = {We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length.},
  language  = {en}
}
@article{ChromikPirlBeilharzetal.2021,
  author    = {Chromik, Jonas and Pirl, Lukas and Beilharz, Jossekin Jakob and Arnrich, Bert and Polze, Andreas},
  title     = {Certainty in QRS detection with artificial neural networks},
  series = {Biomedical signal processing and control},
  volume    = {68},
  journal   = {Biomedical signal processing and control},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1746-8094},
  doi       = {10.1016/j.bspc.2021.102628},
  pages     = {12},
  year      = {2021},
  abstract  = {Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.},
  language  = {en}
}