Erratum: Rodrigo-Mor et al. Principles of Charge Estimation Methods Using High-Frequency Current Transformer Sensors in Partial Discharge Measurements. Sensors 2020, 20, 2520

The authors wish to make the following erratum to this paper [...]

Operation and maintenance

This chapter first provides information on general health and safety rules to be applied by operators in monitoring urban drainage and stormwater management (UDSM) systems, especially in the harsh confined environment of underground sewer systems. Second, it presents experience-based key recommendations for best practice and quality in operation, management and maintenance of sensors and installations for rainfall and discharge measurements. In the last part, three numerical methods (ordinary least squares, Williamson least squares, non-linear regression) are presented, with detailed examples of application, to establish calibration functions which are necessary for all sensors used in UDSM monitoring.

Predictive modelling of the stage-discharge relationship using Gene-Expression Programming

Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional methods are available. This paper presents the Gene-Expression Programming (GEP) modelling technique to accurately model the stage-discharge relationship for the Arouca River in Trinidad and Tobago using only low flow data. The proposed method uses the stage and associated discharge measurements at one cross-section of the Arouca River. These measurements were used to train the GEP model. The results of the GEP model were also compared to the traditional method of the Stage-Discharge Rating Curve (SRC). Four statistical paraments namely the Pearson's Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the GEP model and the SRC method. Overall, the GEP model performed exceptionally well with an R2 of 0.990, RMSE of 0.104, MARE of 0.076 and NSE of 0.957.