Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference

Zaid Tashman, Christoph Gorder, Sonali Parthasarathy, Mohamad M. Nasr-Azadani and Rachel Webre
Additional contact information
Zaid Tashman: Accenture Labs, San Francisco, CA 94105, USA
Christoph Gorder: Charity Water, New York City, NY 10013, USA
Sonali Parthasarathy: Accenture Labs, San Francisco, CA 94105, USA
Mohamad M. Nasr-Azadani: Accenture Labs, San Francisco, CA 94105, USA
Rachel Webre: Charity Water, New York City, NY 10013, USA

Sustainability, 2020, vol. 12, issue 7, 1-16

Abstract: For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water systems can remain in a malfunctioning state for months, forcing communities to return to drinking unsafe water. In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance. To detect anomalies, we need a model of normal water usage behavior first. We train a multilevel probabilistic model of normal usage using approximate variational Bayesian inference to obtain a conditional probability distribution over the hourly water usage data. We then use this conditional distribution to construct a level-1 scoring function for each hourly water observation and a level-2 scoring function for each pump. Probabilistic models and Bayesian inference collectively were chosen for their ability to capture the high temporal variability in the water usage data at the individual pump level as well as their ability to estimate interpretable model parameters. Experimental results in this work have demonstrated that the pump scoring function is able to detect malfunctioning sensors as well as a change in water usage behavior allowing for a more responsive and proactive pump system maintenance.

Keywords: anomaly detection; Bayesian inference; machine learning; water network; pump; well; remote monitoring; sensors; Ethiopia; rural water supply (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/7/2897/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/7/2897/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:7:p:2897-:d:341735

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().