Abstract
Take an AI learning algorithm and a human trainer with an experience in machine intelligence. Take piles of data, in the form of labeled examples. If you think that the task of training a machine that makes accurate decisions is easy as pie, you could not be further from reality. That is what has been missing from this story: getting off on the right foot. For a good start, crucial is the value of data which come large in quantity but low in quality. Beyond how we design our AI, fundamental is making our data valid for learning. We report here our experience in the creation of highly accurate training examples, based on the idea of filtering out all the impurities from a dataset containing 15 million water readings, provided by an Italian water supply company. This was accomplished allowing a human-machine collaboration, down to the implementation of an AI model capable to predict water meter failure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garcia, M.: Racist in the machine: the disturbing implications of algorithmic bias. World Policy J. 33(4), 111–117 (2016). Duke University Press. Project MUSE database. Accessed 20 March 2019
Roccetti, M., Marfia, G., Zanichelli, M.: The art and craft of making the tortellino: playing with a digital gesture recognizer for preparing pasta culinary recipes. Comput. Entertainment 8(4), 1–20 (2010)
Nagar, Y.: Combining human and machine intelligence for making predictions. Doctoral dissertation, Massachusetts Institute of Technology (2013)
Prandi, C., Roccetti, M., Salomoni, P., Nisi, V., Nunes, N.J.: Fighting exclusion: a multimedia mobile app with zombies and maps as a medium for civic engagement and design. Multimedia Tools Appl. 76(4), 4951–4979 (2016)
Salomoni, P., Prandi, C., Roccetti, M., Nisi, V., Nunes, N.J.: Crowdsourcing urban accessibility: some preliminary experiences with results. In: Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter - CHItaly 2015. ACM Press (2015)
Delnevo, G., Roccetti, M., Mirri, S.: Intelligent and good machines? The role of domain and context codification. Mob. Networks Appl. (2019). https://doi.org/10.1007/s11036-019-01233-7
Cabitza, F., Rasoini, R., Gensini, G.F.: Unintended consequences of machine learning in medicine. JAMA 318(6), 517–518 (2017). https://doi.org/10.1001/jama.2017.7797
Walker, D., Creaco, E., Vamvakeridou-Lyroudia, L., Farmani, R., Kapelan, Z., Savic, D.: Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks. In: Procedia Engineering (2015)
Mounce, S.R., Pedraza, C., Jackson, T., Linford, P., Boxall, J.B.: Cloud based ma-chine learning approaches for leakage assessment and management in smart water net-works. In: Procedia Engineering (2015)
Pietrucha-Urbanik, K.: Failure prediction in water supply system–current issues. In: Theory and Engineering of Complex Systems and Dependability, pp. 351–358. Springer (2015)
Haghiabi, A.H., Nasrolahi, A.H., Parsaie, A.: Water quality prediction using machine learning methods. Water Qual. Res. J. 53(1), 3–13 (2018). https://doi.org/10.2166/wqrj.2018.025
Acknowledgments
We are indebted towards the company that has provided the data. To guarantee its privacy, we keep it here anonymized.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Casini, L., Delnevo, G., Roccetti, M., Zagni, N., Cappiello, G. (2020). Deep Water: Predicting Water Meter Failures Through a Human-Machine Intelligence Collaboration. In: Ahram, T., Taiar, R., Colson, S., Choplin, A. (eds) Human Interaction and Emerging Technologies. IHIET 2019. Advances in Intelligent Systems and Computing, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-25629-6_107
Download citation
DOI: https://doi.org/10.1007/978-3-030-25629-6_107
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25628-9
Online ISBN: 978-3-030-25629-6
eBook Packages: EngineeringEngineering (R0)