@article{HempelAdolphsLandwehretal.2020,
  author    = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Janke, David and Amon, Thomas},
  title     = {How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn—classical statistics versus machine learning},
  series = {Sustainability},
  volume    = {12},
  journal   = {Sustainability},
  number    = {3},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2071-1050},
  doi       = {10.3390/su12031030},
  pages     = {18},
  year      = {2020},
  abstract  = {Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20\%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.},
  language  = {en}
}
@article{HempelAdolphsLandwehretal.2020,
  author    = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Willink, Dilya and Janke, David and Amon, Thomas},
  title     = {Supervised machine learning to assess methane emissions of a dairy building with natural ventilation},
  series = {Applied Sciences},
  volume    = {10},
  journal   = {Applied Sciences},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-3417},
  doi       = {10.3390/app10196938},
  pages     = {21},
  year      = {2020},
  abstract  = {A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10\%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.},
  language  = {en}
}