| Yazarlar (1) |
Dr. Öğr. Üyesi Senem GÜNEŞ ŞEN
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Assessing water quality is essential for the sustainable use of freshwater resources, especially under increasing climatic and agricultural pressures. Small irrigation ponds are particularly sensitive to pollution due to their limited buffering capacity. This study evaluates the water quality of the Taşçılar and Yumurtacılar ponds in Kastamonu, Türkiye, by combining conventional Water Quality Indices (WQI) with machine-learning-based interpretation. Physicochemical parameters were measured monthly for one year, and water quality was classified according to WHO and FAO thresholds using the CCME-WQI and weighted arithmetic methods. The integrated approach identified significant differences among standards and highlighted the parameters most responsible for water quality degradation. Machine-learning models improved the interpretation of these indices and supported consistent classification across datasets. The findings emphasize that coupling index-based and data-driven methods can enhance routine monitoring and provide actionable insights for sustainable irrigation-water management, thereby contributing to achieving the SDGs 6, 13, and 15. |
| Anahtar Kelimeler |
| CCME-WQI | decision tree | logistic regression | machine learning | random forest | WA-WQI | water quality index | XGBoost |
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Water Switzerland |
| Dergi ISSN | 2073-4441 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2025 |
| Cilt No | 17 |
| Sayı | 21 |
| Sayfalar | 1 / 24 |
| Doi Numarası | 10.3390/w17213050 |
| Makale Linki | https://doi.org/10.3390/w17213050 |
| Atıf Sayıları |
