Yılmaz, BanuAras, EgemenNacar, Sinan2024-01-252024-01-252018-10-15Yılmaz, B. vd. (2018). ''Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models''. Science of the Total Environment, 639, 826-840.0048-96971879-1026https://www.sciencedirect.com/science/article/pii/S0048969718318011https://hdl.handle.net/11452/39311The 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 Coruh 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 stream flow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. (C) 2018 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessCoruh River BasinHeuristic regressionOptimization algorithmReservoir lifeSupport vector machinesNeural-network modelsPredictionAlgorithmFuzzyAnnSimulationWaveletTreePerformanceErrorsMean square errorOptimizationRegression analysisReservoirs (water)RiversStatistical testsStream flowArtificial bee colonies (ABC)Heuristic regressionMultivariate adaptive regression splinesOptimization algorithmsRiver basinsSimultaneous measurementSuspended sediment loadsTeaching-learning-based optimizationsSuspended sedimentsEnvironmental sciences & ecologyAnimalsBeesEnvironmental monitoringGeologic SedimentsModels, statisticalRegression analysisRiversTurkeyArticle0004368062000822-s2.0-8504726378682684063929803053https://doi.org/10.1016/j.scitotenv.2018.05.153Environmental SciencesPrediction; Flood Forecasting; Water TablesAccuracyAlgorithmArticleArtificial bee colonyInformation processingLinear regression analysisMultivariate adaptive regression splineNonhumanPredictionPriority journalRegression analysisRiverSedimentStatistical analysisStatistical modelSuspended sediment loadTeaching learning based optimizationTurkey (republic)AnimalBeeEnvironmental monitoringPhysiologyProceduresRegression analysisSedimentTurkey (bird)