Szczegóły publikacji
Opis bibliograficzny
Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method / Adam Piasecki, Jakub JURASZ, Jan Franklin Adamowski // Acta Geophysica ; ISSN 1895-6572. — Tytuł poprz.: Acta Geophysica Polonica. — 2018 — vol. 66 iss. 5, s. 1093–1107. — Bibliogr. s. 1106–1107, Abstr. — Publikacja dostępna online od: 2018-08-02. — A. Piasecki - afiliacja: Nicolaus Copernicus University, Toruń
Autorzy (3)
- Piasecki Adam
- AGHJurasz Jakub
- Adamowski Jan Franklin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 117667 |
|---|---|
| Data dodania do BaDAP | 2018-11-07 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s11600-018-0183-5 |
| Rok publikacji | 2018 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Acta Geophysica |
Abstract
Lake waters are a significant source of drinking water and contribute to the local economy (e.g. enabling irrigation, offering opportunities for tourism, waterways for transport, and meeting utility water demands); therefore, the ability to accurately forecast lake water levels is important. However, given the significant lack of research with respect to forecasting water levels in small lakes (i.e. 0.05 km2 < area < 10 km2), the present study sought to address this knowledge gap by testing a pair of hypotheses: (1) it is possible to forecast water levels in small surface lakes using artificial neural networks (ANN), and (2) better water-level forecasts will be obtained when the wavelet transform (WT) is used as an input data pre-processing tool. Based on an analysis of a case study in Lake Biskupinskie (1.16 km2) in Poland and based on a range of model performance statistics (e.g. mean absolute error, root mean square error, mean squared error, coefficient of determination, mean absolute percentage error), both hypotheses were confirmed for monthly forecasting of lake water levels. ANNs provided good forecasting results, and WT pre-processing of input data led to even better forecasts. Additionally, it was found that meteorological variables did not have a significant impact in forecasting water-level fluctuations. In light of the results and the limited scope of the present study, proposed future research directions and problems to be resolved are discussed.
