Abstract In this paper, we describe the Removers design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement.Our initial attempt to train a recurrent deep neural network (RNN), based on the use of 15 million of readings gathered from 1 million of mechanical water meters, spread throughout Northern Italy, led to non-positive results.We learned this was due to a lack of specific attention devoted to the quality of the analyzed data.We, hence, developed a novel methodology, based on a new semantics which we enforced on the training data.
This allowed us to extract only those samples which are representative of the complex phenomenon of defective water meters.Adopting such a methodology, the accuracy of our RNN exceeded the 80% threshold.We simultaneously realized that the new training dataset differed significantly, in Bluetooth Hands-Free Kit statistical terms, from the initial dataset, leading to an apparent paradox.Thus, with our contribution, we have demonstrated how to reconcile such a paradox, showing that our classifier can help detecting defective meters, while simplifying replacement procedures.