Comparing Artificial Neural Networks and Regression-based Methods for Modeling Daily Dissolved Oxygen Concentration: A Study Based on Long-term Monitored Data
Yazarlar (3)
Doç. Dr. Sinan NACAR
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Karadeniz Teknik Üniversitesi, Türkiye
Karadeniz Teknik Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | KSCE Journal of Civil Engineering |
| Dergi ISSN | 1226-7988 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q3 |
| Makale Dili | Türkçe |
| Basım Tarihi | 08-2024 |
| Cilt No | 28 |
| Sayı | 11 |
| Sayfalar | 4813 / 4824 |
| DOI Numarası | 10.1007/s12205-024-2613-z |
| Makale Linki | https://doi.org/10.1007/s12205-024-2613-z |
| Özet |
| In this study, the ability of regression-based methods, namely conventional regression analysis (CRA) and multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs) method was investigated to model the river dissolved oxygen (DO) concentration. Daily average data for discharge and water-quality (WQ) indicators, which include DO concentration, temperature, specific conductance, and pH, were provided for the monitoring stations USGS 14210000 (upstream) and USGS 14211010 (downstream) in the Clackamas River, Oregon, USA. Eight models were established using different combinations of the input parameters and tested to determine the contribution of each parameter used in the modeling to the performance of the models. The results of the models and methods were compared with each other using several performance statistics. Although the performances of the methods were … |
| Anahtar Kelimeler |
| Artificial neural networks | Clackamas river | Dissolved oxygen concentration | Modeling | Multivariate adaptive regression splines | Water quality |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 3 |
| Google Scholar | 5 |