A Temporal Forecasting Driven Approach Using Facebook's Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System

Smart sensor systems play a decisive role in the condition assessment of concrete sewer pipes going through microbial corrosion. Few Australian water utilities adopt a predictive analytic model for estimating the corrosion. They require sensor inputs like sewer air temperature data for corrosion pre...

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Bibliographic Details
Main Authors: Thiyagarajan, Karthick, Kodagoda, Sarath, Ulapane, Nalika, Prasad, Mukesh
Format: Conference Proceeding
Language:English
Subjects:
Anomaly detection
ARIMA
Atmospheric modeling
Bagged model
concrete corrosion
Corrosion
ETS model
Facebook Prophet
Forecasting
Monitoring
Predictive models
sewer pipe
TBATS model
temperature sensor
Temperature sensors
time series model
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Description
Summary:Smart sensor systems play a decisive role in the condition assessment of concrete sewer pipes going through microbial corrosion. Few Australian water utilities adopt a predictive analytic model for estimating the corrosion. They require sensor inputs like sewer air temperature data for corrosion prediction. A sensor system was developed to monitor the daily variation of sewer air temperature inside the harsh sewer environmental conditions. However, a diagnostic tool to evaluate the streaming sensor data is vital for reliable monitoring. In this context, this paper proposes a temporal forecasting driven approach for anomaly detection in sewer air temperature sensor system. Several temporal forecasting models were comprehensively evaluated and adopted Facebook's Prophet method based forecasting to develop an anomaly detection approach. The proposed approach was evaluated with sewer air temperature sensor data and the results indicate a reasonable anomaly detection performance.
ISSN:2158-2297
DOI:10.1109/ICIEA48937.2020.9248142