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

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.

Pollution Source Localization in Wastewater Networks

“…In recent years, several project initiatives [ 6 , 7 ] and prototypes of sensor systems [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] for wastewater monitoring have been proposed and studied. These include the design of sensors (electrochemical sensors, optical sensors, mass spectrometry, ion spectrometry, etc.)…”

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

“…Researchers’ interest in exploring methods for the detection, localization, and quantification of pollutants in wastewater networks (WWNs) and water distribution systems (WDSs) has grown considerably in recent years, especially regarding sensor technology [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Questionable policies regarding waste disposal are prevalent in today’s industry [ 17 , 18 ].…”

A Study of Automatic Metrics for the Evaluation of Natural Language Explanations

“…These explanations were subsequently rated for Clarity and Informativeness, two subjective ratings previously used for NLG evaluations (Gatt and Krahmer, 2018;. The motivation behind using BN is that they are reasonably easy to interpret, are frequently used for the detection of anomalies in the data (Tashman et al, 2020;Saqaeeyan et al, 2020;Metelli and Heard, 2019;Mascaro et al, 2014), and have been used to approximate deep learning methods (Riquelme et al, 2018;Gal and Ghahramani, 2016), which we could, in turn, explain in Natural Language.…”