Prediction of electrical conductivity using ANN and MLR: a case study from Turkey
Yazarlar (5)
Prof. Dr. Tülay EKEMEN KESKİN Karabük Üniversitesi, Türkiye
Emre Özler Karabük Üniversitesi, Türkiye
Emrah Şander Karabük Üniversitesi, Türkiye
Prof. Dr. Muharrem DÜĞENCİ Karabük Üniversitesi, Türkiye
Mohammed Yadgar Ahmed Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Acta Geophysica (Q4) | ||
| Dergi ISSN | 1895-6572 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2020 |
| Kabul Tarihi | 28-03-2020 | Yayınlanma Tarihi | 18-05-2020 |
| Cilt / Sayı / Sayfa | 68 / 3 / 811–820 | DOI | 10.1007/s11600-020-00424-1 |
| Makale Linki | https://link.springer.com/10.1007/s11600-020-00424-1 | ||
| UAK Araştırma Alanları |
Hidrojeoloji
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| Özet |
| The study areas are located in Turkey (Kastamonu, Bartın, Karabük, Sivas) and contain very different rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have different chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difficult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artificial neural networks (ANN) and multiple linear regressions (MLR). Twelve different hydrochemical parameters, which affect the EC, such as major/minor ions and trace elements, were used in the analysis … |
| Anahtar Kelimeler |
| Artificial neural network (ANN) | Multiple linear regression (MLR) | Prediction of EC | Water quality parameters |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 20 |
| Scopus | 21 |
| Google Scholar | 31 |