Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models

• Published in: The Science of the total environment Vol. 639; pp. 826 - 840
• Main Authors: Yilmaz, Banu, Aras, Egemen, Nacar, Sinan, Kankal, Murat
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.10.2018
• Subjects: Animals; Bees - physiology; Environmental Monitoring - methods; Geologic Sediments; Heuristic regression; Models, Statistical; Optimization algorithm; Regression Analysis; Reservoir life; Rivers; Turkey; Çoruh River Basin; Turkey; Çoruh River Basin; Optimization algorithm; Heuristic regression; Reservoir life
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2018.05.153

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. [Display omitted] •Implementation of different regression models to estimate SSL in Çoruh River Basin.•MARS, TLBO, ABC and CRA techniques were developed for two different stations.•Two different data sets were used to reinforce the validity of model successes.•Performance of MARS's method is more successful than the others in SSL prediction.