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Automating the raw data to model input process using flexible open source tools

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Abstract
The availability of dynamic influent data is of crucial importance for model development, as it provides the model input needed realistic dynamic simulations. Data analysis and reconciliation of such data are however often very time-consuming tasks, making that, even when some online influent data is indeed available, the option is often chosen to generate influent data in one way or the other. A lot of information contained in the available data is lost in that way. This contribution showcases a python package that allows for a streamlined data analysis workflow and provides possibilities for data analysis, validation and gap filling, with as main goal to recover and use as much (influent) data as possible. In the end, this provides a means towards more scientifically sound dynamic simulations and model calibration and validation, while limiting the time spent on data reconciliation.
Keywords
Data analysis, Modelling, Python package, INFLUENT DATA, SIMULATION, QUALITY

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Chicago
De Mulder, Chaïm, Tony Flameling, Jeroen Langeveld, Youri Amerlinck, Stefan Weijers, and Ingmar Nopens. 2017. “Automating the Raw Data to Model Input Process Using Flexible Open Source Tools.” In Lecture Notes in Civil Engineering, ed. Giorgio Mannina, 4:92–97. Cham, Switzerland: Springer.
APA
De Mulder, Chaïm, Flameling, T., Langeveld, J., Amerlinck, Y., Weijers, S., & Nopens, I. (2017). Automating the raw data to model input process using flexible open source tools. In Giorgio Mannina (Ed.), Lecture Notes in Civil Engineering (Vol. 4, pp. 92–97). Presented at the 2017 Frontiers international conference on Wastewater Treatment (FICWTM 2017), Cham, Switzerland: Springer.
Vancouver
1.
De Mulder C, Flameling T, Langeveld J, Amerlinck Y, Weijers S, Nopens I. Automating the raw data to model input process using flexible open source tools. In: Mannina G, editor. Lecture Notes in Civil Engineering. Cham, Switzerland: Springer; 2017. p. 92–7.
MLA
De Mulder, Chaïm, Tony Flameling, Jeroen Langeveld, et al. “Automating the Raw Data to Model Input Process Using Flexible Open Source Tools.” Lecture Notes in Civil Engineering. Ed. Giorgio Mannina. Vol. 4. Cham, Switzerland: Springer, 2017. 92–97. Print.
@inproceedings{8521236,
  abstract     = {The availability of dynamic influent data is of crucial importance for model development, as it provides the model input needed realistic dynamic simulations. Data analysis and reconciliation of such data are however often very time-consuming tasks, making that, even when some online influent data is indeed available, the option is often chosen to generate influent data in one way or the other. A lot of information contained in the available data is lost in that way. This contribution showcases a python package that allows for a streamlined data analysis workflow and provides possibilities for data analysis, validation and gap filling, with as main goal to recover and use as much (influent) data as possible. In the end, this provides a means towards more scientifically sound dynamic simulations and model calibration and validation, while limiting the time spent on data reconciliation.},
  author       = {De Mulder, Cha{\"i}m and Flameling, Tony and Langeveld, Jeroen and Amerlinck, Youri and Weijers, Stefan and Nopens, Ingmar},
  booktitle    = {Lecture Notes in Civil Engineering},
  editor       = {Mannina, Giorgio},
  isbn         = {9783319584201},
  issn         = {2366-2557},
  language     = {eng},
  location     = {Palermo, Italy},
  pages        = {92--97},
  publisher    = {Springer},
  title        = {Automating the raw data to model input process using flexible open source tools},
  url          = {http://dx.doi.org/10.1007/978-3-319-58421-8\_14},
  volume       = {4},
  year         = {2017},
}

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