Water Contamination Detection With Binary Classification Using Artificial Neural Networks

Sammanfattning

Water contamination is a major source of diseasearound the world. Therefore, the reliable monitoring of harmfulcontamination in water distribution networks requires considerableeffort and attention. It is a vital necessity to possess a reliablemonitoring system in order to detect harmful contamination inwater distribution networks. To measure the potential contamination,a new sensor called an ’electric tongue’ was developedin Link¨opings University. It was created for the purpose ofmeasuring various features of the water reliably. This projecthas developed a supervised machine learning algorithm that usesan artificial neural network for the detection of anomalies in thesystem. The algorithm can detect anomalies with an accuracy ofaround 99.98% based on the data that was available. This wasachieved through a binary classifier, which reconstructs a vectorand compares it to the expected outcome. Despite the limitationsof the problem and the system’s capabilities, binary classificationis a potential solution to this problem.