Szczegóły publikacji
Opis bibliograficzny
Potential application of machine learning techniques to identify prior limiting factors as a basis for eutrophication assessment / IRFAN Ali, Elena NEVEROVA-DZIOPAK, Tamas Buday, Zbigniew KOWALEWSKI // Sustainability [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2071-1050 . — 2026 — vol. 18 iss. 2 art. no. 841, s. 1–25. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 23–25, Abstr. — Publikacja dostępna online od: 2026-01-14
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165835 |
|---|---|
| Data dodania do BaDAP | 2026-02-04 |
| Tekst źródłowy | URL |
| DOI | 10.3390/su18020841 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sustainability |
Abstract
The aim of this study was to determine the factors that influence eutrophication. The factors causing eutrophication are widely known, but identifying the primary threat for a specific water body remains challenging. The study objects were the warm monomictic urban Dal Lake in Kashmir, India, and the artificial dam reservoir Dobczyce in Poland. Data analysis methods, including multiple regression and artificial neural networks (NNET and NeuralNet) from the R package [ver. 4.5.2], were used. Regarding Dal Lake, the factor most influencing the trophic change was total nitrogen. In contrast, for the Dobczyce dam reservoir, water temperature was the dominant factor. Although the regression method did not provide clear results, neural networks enabled the identification of the limiting factors; therefore, the proposed approach may be useful for determining the factors limiting the eutrophication process. The core novelty of this research lies in demonstrating the potential of artificial neural networks to identify key factors causing eutrophication, particularly under conditions of limited data.
