A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed.

Separate sewer systems are sensitive to illegal or mis-connections. Several techniques (including the Distributed Temperature Sensor) are now available to identify and locate those connections. Based on thermal fingerprints, DTS allows the localization of each lateral connection along a reach. The use of Infra-Red camera has been investigated with 748 laboratory experiments (artificial connections along a flume). The tested connections vary in diameters (from 75 to 200 mm), lengths of intrusion (from 0 to 200 m), shapes (circular or linear i.e. cracks), depths, discharge rates between the lateral connection and the main flume, and temperatures. IR frame analysis (for detection) and 2D temperature mapping (at the free water surface, for quantification) demonstrate that: i) the detection limit is very low (ratio between lateral and main discharges: 0.025) and ii) the quantification of the lateral discharge is impossible. Application of an IR camera seems to be a promising technique to detect lateral connections.

Monitoring sewer sediments is necessary to better understand sedimentation and erosion processes. Sonar is one of the available techniques to proceed to sewer sediment measurements. Extraction of numerical data, implementation of a new algorithm to identify the water-sediment interface, laboratory and field experiments have been done to evaluate the device, to quantify uncertainties and to test the sonar under various conditions. Results demonstrate that: 1) uncertainties in repeatability are less than 4%, 2) the sonar delivers accurate results under various conditions (small to large sewers and grit chambers), and 3) in situ measurements are affected by uncertainties, mainly due to the fact that the sensor is not in a fixed position but is floating on the free surface in the sewer. This device is useful and accurate for semi-automatic measurement but further research and improvements should be done to better know the position of the device in the section.

UV/Vis spectrophotometers have been used to monitor water quality since the early 2000s. Calibration of these devices requires sampling campaigns to elaborate relations between recorded spectra and measured concentrations. In order to build robust calibration data sets, several spectra must be recorded per sample. This study compares two approaches – principal component analysis and data depth theory – to identify outliers and select the most representative spectrum (MRS) among the repetitively recorded spectra. Detection of samples that contain outliers is consistent between the methods in more than 70% of the samples. Identification of spectra as outliers is consistent in more than 95% of the cases. The identification of MRS differs depending on the approach used. In their current form, both of the proposed approaches can be used for outlier detection and identification. Further studies are suggested to combine the methods and develop an automated ranking and sorting system.