State-of-the-art in the field of well field optimization modelling


Work package WP 5.2 “Combination of Managed Aquifer Recharge (MAR) and adjusted conventional treatment processes for an Integrated Water Resources Management“ within the European Project TECHNEAU (“Technology enabled universal access to safe water”) investigates bank filtration (BF) + post-treatment as a MAR technique to provide sustainable and safe drinking water supply to developing and newly industrialised countries. One of the tasks within the project was the identification of state-of-the-art tools in the field of well field optimization modelling. Most of the currently used tools are process-driven simulation models like MODFLOW or FEFLOW. These are sometimes also combined with optimization models to reduce the computational demand and are utilized as strategic planning tools for water supply managers. However, in case of optimizing well field operation (i) under relatively constant boundary conditions and (ii) enough field data (temporal and spatial resolution dependent of the dynamics of the state parameter of interest, e.g. groundwater table, contaminant concentrations) data-driven approaches like support vector machines (SVM) can be used instead. If the water manager’s key interest is only a good predictive capability in combination with low computational demand, the application of this approach is more goal-orientated to simulate the dynamics of well field performance indicators efficiently. The contents of this report were presented to possible end-users, experts from Berliner Wasserbetriebe and Veolia. In agreement with their recommendations it was decided to focus further research within TECHNEAU on the empirical, data driven modelling approach. The selected approach is currently tested in the framework of a diploma thesis for a Berlin waterworks with the objective to analyse available production and observation well hydrographs by using modern statistical methods like principal component analysis and SVM (

Kompetenzzentrum Wasser Berlin gGmbH
Rustler, M.
Rustler, M.
Data scientist

My research interests include reproducible research, data management and programming (R & Python).