Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland
Artykuł w czasopiśmie
MNiSW
40
Lista 2021
| Status: | |
| Autorzy: | Kulisz Monika, Kujawska Justyna |
| Dyscypliny: | |
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| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 2130 |
| Numer artykułu: | 012028 |
| Strony: | 1 - 11 |
| Scopus® Cytowania: | 13 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | VI International Conference of Computational Methods in Engineering Science |
| Skrócona nazwa konferencji: | CMES 2021 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 25 listopada 2021 do 27 listopada 2021 |
| Miasto konferencji: | Zamość |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 13 grudnia 2021 |
| Abstrakty: | angielski |
| The aim of this paper is to present the potential of using neural network modelling for the prediction of the surface water quality index (WQI). An artificial neural network modelling has been performed using the physicochemical parameters (TDS, chloride, TH, nitrate, and manganese) as an input layer to the model, and the WQI as an output layer. The physicochemical parameters have been taken from five measuring stations of the river Warta in the years 2014-2018 via the Chief Inspectorate of Environmental Protection (GIOŚ). The best results of modelling were obtained for networks with 5 neurons in the hidden layer. A high correlation coefficient (general and within subsets) 0.9792, low level of MSE in each subset (training, test, validation), as well as RMSE at a level of 0.624507639 serve as a confirmation. Additionally, the maximum percentage of an error for WQI value did not exceed 4%, which confirms a high level of conformity of real data in comparison to those obtained during prediction. The aforementioned results clearly present that the ANN models are effective for the prediction of the value of the Surface water quality index and may be regarded as adequate for application in simulation by units monitoring condition of the environment. |
