Deep Water: Predicting Water Meter Failures Through a Human-Machine Intelligence Collaboration

Casini, Luca; Delnevo, Giovanni; Roccetti, Marco; Zagni, Nicolò; Cappiello, Giuseppe

doi:10.1007/978-3-030-25629-6_107

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have reconciled this paradox, showing how an adequate use of our classifier can help the company that provided the initial data to detect the meters to be replaced, at a lower cost than that previously paid when different and more expensive procedures were in use. This completes our controversial journey that began almost a year ago and of which some very preliminary studies can be also found in [25,26].…”

Section: Discussionmentioning

confidence: 53%

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

et al. 2019

Self Cite

View full text Add to dashboard Cite

If modern artificial intelligence (AI) comes often misunderstood, this is mainly due to the fact that, historically, it is solely tied to the way human brains work and think. New machine learning (ML) algorithms, instead, learn now by processing massive piles of data. This process enables machines to adapt to real-world situations, as well as to propose suggestions on how to classify and interpret a variety of different real phenomena. Simply speaking, the deployment of modern ML systems into critical applications is directly influenced by the way training data are organized and modeled [1-3]. Hence, while those modern algorithms rapidly sift through huge datasets, loaded with millions of information, a thoughtfully designed AI, beyond its ML-based core, should never disregard the fact that algorithms that learn are, for now, just another form of machine instruction, still guided and influenced by the potential and the limitations that training data carry with them. In other words, even when we train algorithms to learn basic associations that can then be used to approximate,

show abstract

Section: Discussionmentioning

confidence: 53%

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, the most accurate machine learning algorithm has been exploited to create a library, named InspectNoise, that can be used by similar IoT platforms (equipped with low-cost microphones), supporting them in performing accurate noise pollution monitoring activities, in terms of sensing decibels. Another interesting point of attention is selecting the most appropriate dataset for adequately training a system by means of artificial intelligence strategies, as discussed in [ 39 ] and in [ 40 ]. In fact, in order to improve the quality of our training methodology, we have included also environmental data (such as temperature, humidity, air pollutant agents, and so on, collected by means of a multi-sensor station).…”

Section: Introductionmentioning

confidence: 99%

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Monti

Vincenzi

Mirri

et al. 2020

Sensors

View full text Add to dashboard Cite

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

show abstract

Dimensionality Reduction and the Strange Case of Categorical Data for Predicting Defective Water Meter Devices

Roccetti

Casini

Delnevo

et al. 2020

Human Interaction, Emerging Technologies and Future Applications III

View full text Add to dashboard Cite

Deep Water: Predicting Water Meter Failures Through a Human-Machine Intelligence Collaboration

Cited by 4 publications

References 10 publications

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Dimensionality Reduction and the Strange Case of Categorical Data for Predicting Defective Water Meter Devices

Contact Info

Product

Resources

About