Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method
| cris.lastimport.wos | 2025-10-23T06:54:58Z | |
| cris.virtual.author-orcid | 0000-0002-1453-0374 | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | 917b05fe-6da6-4828-82f0-08b7c58485fd | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| dc.abstract.en | The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites. | |
| dc.affiliation | Wydział Inżynierii Środowiska i Inżynierii Mechanicznej | |
| dc.affiliation.institute | Katedra Melioracji, Kształtowania Środowiska i Gospodarki Przestrzennej | |
| dc.contributor.author | Amnuaylojaroen, Teerachai | |
| dc.contributor.author | Ptak, Mariusz | |
| dc.contributor.author | Sojka, Mariusz | |
| dc.date.access | 2025-03-27 | |
| dc.date.accessioned | 2025-03-27T08:48:24Z | |
| dc.date.available | 2025-03-27T08:48:24Z | |
| dc.date.copyright | 2024-11-17 | |
| dc.date.issued | 2024 | |
| dc.description.abstract | <jats:p>The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites.</jats:p> | |
| dc.description.accesstime | at_publication | |
| dc.description.bibliography | il., bibliogr. | |
| dc.description.finance | publication_nocost | |
| dc.description.financecost | 0,00 | |
| dc.description.if | 3,0 | |
| dc.description.number | 22 | |
| dc.description.points | 100 | |
| dc.description.version | final_published | |
| dc.description.volume | 16 | |
| dc.identifier.doi | 10.3390/w16223296 | |
| dc.identifier.issn | 2073-4441 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/2629 | |
| dc.identifier.weblink | https://www.mdpi.com/2073-4441/16/22/3296 | |
| dc.language | en | |
| dc.pbn.affiliation | environmental engineering, mining and energy | |
| dc.relation.ispartof | Water (Switzerland) | |
| dc.relation.pages | art. 3296 | |
| dc.rights | CC-BY | |
| dc.sciencecloud | send | |
| dc.share.type | OPEN_JOURNAL | |
| dc.subject.en | neural network | |
| dc.subject.en | air temperature | |
| dc.subject.en | wind | |
| dc.subject.en | water temperature | |
| dc.subject.en | SHAP | |
| dc.title | Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 22 | |
| oaire.citation.volume | 16 |