Automating the Raw Data to Model Input Process Using Flexible Open Source Tools
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.
KeywordsData analysis Modelling Python package
The authors would like to express their gratitude towards Waterboard De Dommel for both the funding of this research and the smooth cooperation.
- Langeveld J, Schilperoort R, Van Daal P, Benedetti L, Amerlinck Y, de Jonge J, Flameling T, Nopens I, Weijers S (2014) A new empirical sewer water quality model for the prediction of WWTP influent quality. In: Proceedings of the 13th conference on urban drainageGoogle Scholar
- McKinney W (2010) Data structures for statistical computing in python. In: van der Walt S, Millman J (eds) Proceedings of the 9th Python in science conference, pp 51–56Google Scholar