Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models
Yazarlar (1)
| Makale Türü | Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği, araştırma notu, bilirkişi raporu ve benzeri) |
| Makale Alt Türü | Diğer hakemli ulusal dergilerde yayınlanan teknik not, editöre mektup, tartışma, vaka takdimi ve özet türünden makale |
| Dergi Adı | SCIENCE OF THE TOTAL ENVIRONMENT |
| Dergi ISSN | 0048-9697 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2018 |
| Cilt No | 639 |
| Sayfalar | 826 / 840 |
| DOI Numarası | 10.1016/j.scitotenv.2018.05.153 |
| Makale Linki | https://www.sciencedirect.com/science/article/abs/pii/S0048969718318011?via%3Dihub |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Heuristic regression | Optimization algorithm | Reservoir life | Çoruh River Basin |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 77 |
| Google Scholar | 102 |