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@MASTERSTHESIS{Gramlich:906244,
      author       = {Gramlich, Vincent},
      title        = {{D}eep learning methods for forecasting of extreme ambient
                      ozone values},
      school       = {University of Cologne},
      type         = {Masterarbeit},
      reportid     = {FZJ-2022-01320},
      pages        = {59 p.},
      year         = {2021},
      note         = {Masterarbeit, University of Cologne, 2021},
      abstract     = {Exposure to high ozone concentrations can be harmful for
                      humans and therefore many countries have declared a
                      threshold for ozone concentrations that should not be
                      exceeded. That is why the ability to predict high and
                      extreme near surface ozone concentrations is important not
                      only for human health but also for regulatory purposes. The
                      problem with many existing ozone forecasting methods,
                      especially deep learning approaches, is their inaccuracy and
                      unreliability to forecast high ozone concentrations. The
                      goal of this study is to discover the usage of oversampling
                      and subsequent finetuning to increase the forecast precision
                      for extreme near surface ozone concentration. Therefore the
                      architecture and experiment setup of IntelliO3-ts, a
                      convolutional neural network for the forecast of near
                      surface ozone concentrations, is used as a foundation to
                      which the methods are applied. At first, oversampling is
                      applied to the data set, which is the process of multiplying
                      samples from less frequent ozone concentration ranges and
                      adding them to the data set. The thereby obtained new
                      "oversampled" data set that has a flatter sample
                      distribution is then used to train the neural network. In a
                      second and additional step the finetuning takes place, which
                      is a retraining of the network obtained in the first step,
                      using the original data set before oversampling was applied.
                      For both methods different parameters will be tested and
                      evaluated on the basis of different scores calculated on 2x2
                      contingency tables. The contingency tables are created by
                      using a threshold and separating the test data in two
                      groups, ozone concentrations below and above the threshold.
                      The oversampling increases the ability to successfully
                      forecast if a sample exceeds a certain threshold, thereby
                      increasing the forecast precision for high ozone values.
                      These advantages come at the cost of also increasing the
                      percentage of samples that are falsely predicted to be above
                      a certain threshold, also resulting in a systematic
                      overestimation. The best model obtained, was able to
                      increase the hit rate at 60 ppb from $43\%$ to $67\%$ and at
                      80 ppb from $1:9\%$ to $15.2\%.$ This means that the modelis
                      able to correctly predict that a sample is above 60 ppb and
                      80 ppb for $67\%$ and $15.2\%$ of all samples above that
                      threshold instead of only achieving this for $43\%$
                      $and1.9\%$ of the samples above that threshold,
                      respectively. Therefore the oversampling offers a valuable
                      trade off, to sacrifice parts of the overall performance in
                      order to increase the ability to forecast high ozone values,
                      which might be useful especially for regulatory purposes.
                      The finetuning did not add any new value to that, but only
                      reverted some of the improvements that were achieved by the
                      oversampling. For future research it might be interesting to
                      investigate the usage of different oversampling methods or
                      explore the application of oversampling and finetuning to
                      the forecasting of multiple days, as this study only focused
                      on the forecast for the next day.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://juser.fz-juelich.de/record/906244},
}