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

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.

Evaluation of Battery-free UHF-RFID Sensor Wireless Signals for In-pipe Robotic Applications

“…Corrosion is a significant cause of structural degradation in those pipelines. Water utilities assess corrosion conditions using a variety of sensing technologies [1]- [7] in order to avert catastrophic pipe failures. To extend the useful service life of the pipeline and reduce costs associated with pipe replacement, severely corroded pipes are replaced, while moderately corroded pipes are applied with protective linings [8].…”

Application of Facebook’s Prophet Model for Forecasting Meteorological Data

“…풍속, 가뭄인자 등)에 따라 산불발생 확률 또는 위험정도를 계산하는데 초점이 맞춰져 있다 (Lee et al, 2004 (Yu et al, 2006;Kim et al, 2020 (Taylor and Letham, 2018;AshaEmail et al, 2020;Thiyagarajan et al, 2020) (Cho et al, 2019;Kim et al, 2020)되고 있는 Index of Agreement (IOA, Eq. (2)), Mean Error (Eq.…”