Microbial corrosion is considered the main reason for multi-billion
dollar sewer asset degradation. Sewer pipe surface temperature is a
vital parameter for predicting the micro-biologically induced concrete
corrosion. Due to this important measure, a surface temperature sensor
suite was recently developed and tested in an aggressive sewer
environment. The sensors can fail and they may also put offline during
the period of scheduled maintenance. In such situations, time series
forecasting of sensor data can be an alternative measure for the
operators managing the sewer network. In this regard, this paper focuses
on the short-term forecasting of sensor measurements. The evaluation was
carried out by forecasting the sensor measurements for different time
periods and evaluated with different forecasting models. The ETS model
leads to high short-term forecasting accuracy and the ARIMA model leads
to high long-term forecasting accuracy. The models were evaluated on
real data captured in a Sydney sewer