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Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

cris.virtual.author-orcid0000-0002-0929-5285
cris.virtual.author-orcid0000-0001-9616-6184
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cris.virtualsource.author-orcida59124fb-16e6-4640-b90f-907536b11f95
cris.virtualsource.author-orcid42c13a44-7add-43d0-b4d6-fca88ee69d61
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dc.abstract.enIt is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Budownictwa i Geoinżynierii
dc.affiliation.instituteKatedra Melioracji, Kształtowania Środowiska i Gospodarki Przestrzennej
dc.contributor.authorAgaj, Tropikë
dc.contributor.authorBudka, Anna
dc.contributor.authorJanicka, Ewelina
dc.contributor.authorBytyqi, Valbon
dc.date.access2025-01-29
dc.date.accessioned2025-01-29T08:06:24Z
dc.date.available2025-01-29T08:06:24Z
dc.date.copyright2024-09-28
dc.date.issued2024
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_research
dc.description.financecost10443,69
dc.description.if3,8
dc.description.points140
dc.description.versionfinal_published
dc.description.volume14
dc.identifier.doi10.1038/s41598-024-73405-9
dc.identifier.issn2045-2322
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/2411
dc.identifier.weblinkhttps://www.nature.com/articles/s41598-024-73405-9
dc.languageen
dc.pbn.affiliationenvironmental engineering, mining and energy
dc.relation.ispartofScientific Reports
dc.relation.pagesart. 22444
dc.rightsCC-BY-NC-ND
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.enwater level
dc.subject.entime series models
dc.subject.enland use
dc.subject.enestimate
dc.titleUsing ARIMA and ETS models for forecasting water level changes for sustainable environmental management
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume14