Comparison of OLS, ANN, KTRL, KTRL2, RLOC, and MOVE as Record-Extension Techniques for Water Quality Variables

Abstract

In this study, nine record extension techniques were explored: ordinary least squares (OLS), maintenance of variance extension techniques (MOVE1, MOVE2, MOVE3, and MOVE4), Kendall–Theil robust line (KTRL), artificial neural network (ANN), and two recently developed techniques (RLOC and KTRL2). The first technique is the robust line of organic correlation (RLOC), which is a modified version of MOVE1 with the advantage of being robust in the presence of outliers and/or deviation from normality. The second technique is a modified version of the KTRL (KTRL2) that has the advantage of being able to maintain the variance in the extended records. Water quality data from the Nile Delta monitoring network in Egypt were used to conduct an empirical experiment. The nine record extension techniques were used to extend the Chloride records using Electric Conductivity as a predictor. A comparison was carried out between the nine techniques to assess their ability to provide extended records that preserve different statistical characteristics of the observed records. Results showed that the RLOC and KTRL2 are better than other techniques in preserving the characteristics of the entire distribution. However, the ANN and KTRL techniques are superior in estimating individual water quality records. The RLOC or KTRL2 techniques are recommended for extending records of discontinued water quality variables in the Nile Delta, while the ANN and KTRL techniques are recommended for the substitution of missing values. In addition, a Monte Carlo experiment was conducted to assess the impact of the presence of outliers on the performance of the MOVE techniques as well as the KTRL2 and RLOC. Results of the Monte Carlo experiment showed that, in the presence of outliers, the KTRL2 and RLOC techniques outperform the MOVE techniques.