Decision Support System for Bank Filtration Systems


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 is the development of a Decision Support System (DSS) to assess the feasibility of BF systems under varying boundary conditions such as: (i) quality of surface and ambient groundwater, (ii) local hydrological and hydrogeological properties (e.g. clogging layer) and (iii) well field design (distance to bank) and operation (pumping rates). Since the successful, cost-effective implementation of BF systems requires the optimization of different objectives such as (i) optimizing the BF share in order to maintain a predefined raw water quality or (ii) maintaining a predefined minimum travel time between bank and production well, both aspects are addressed within the DSS. As an example for a practical application the DSS is tested with data from the Palla well field in Delhi/India. As a result optimal shares of bank filtrate were calculated for the monsoon and non-monsoon season. By simulating different pumping and clogging scenarios with the BF Simulator optimal pumping rates were derived. The DSS proved to be a good qualitative tool to identify and learn about the trade-offs a decision maker has to make due to the (i) inherently competing nature of different objectives (e.g. high BF share and minimum travel time > 50 d) and the (ii) inherent uncertainty due to the large natural variability of boundary conditions (e.g. clogging layer). Since both characteristics can be addressed within the DSS it helps to add transparency and reproducibility to the decision making process. An additional advantage is that its application requires only low effort concerning time, money, and manpower. Thus the application of the DSS is recommended to accompany decision making processes especially in developing and newly industrialised countries where data availability and low financial budgets are usually the major burden for the application of more complex, data-demanding decision support tools. However, it needs to be considered that in practice additional parameters like water availability, energy efficiency and cost-benefit need to be taken into account.

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

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