A Temporal Forecasting Driven Approach Using Facebook's Prophet Method
for Anomaly Detection in Sewer Air Temperature Sensor System
- Karthick Thiyagarajan ,
- sarath kodagoda ,
- Nalika Ulapane ,
- mukesh prasad
Abstract
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.