Drinking water can become contaminated through several mediums, the distribution network being a major one. Sewage water may enter the pipe network through pipe breaks or leaks, and there are many documented cases historically of this causing people to fall ill. The drinking water in the distribution network is currently monitored through manual sampling, which is time-consuming and infrequent. This means contaminations in the network are usually not discovered until after people have already fallen ill. There is therefore a need for detecting contaminations in real time in the distribution network before people become sick.

In this project methods for detecting sewage contamination in the drinking water distribution network are explored and evaluated. It investigates the feasibility of using a sensor called the electronic tongue alongside one of five different algorithms to detect contaminations in real time. The most important factors include being able to detect low concentrations of sewage as well as minimizing false alarms, and a delay in the response is also introduced to reduce the number of false alarms. The algorithms are evaluated with regard to the trade-off between contamination detection, false alarm rate and response delay, and the results show that out of the five algorithms, two are viable for detecting sewage contaminations using the electronic tongue. The thesis highlights the decisions that need to be made when tackling this problem and provides some alternatives for solving it. To evaluate the real efficacy of the algorithms, further testing with more nuanced data is required, especially in real scenarios.