Machine learning-based modeling of surface water temperature dynamics in arctic lakes
Hyung Il Kim1,2, Dongkyun Kim3, Mohammad Milad Salamattalab4
1DL E&C, Civil Business Division, Donuimun, D Tower, 134 Tongil-Ro, Jongno-Gu, Seoul, Korea.
Environmental Science and Pollution Research International
|October 3, 2024
Summary
Arctic lakes are warming rapidly. Machine learning models, particularly long-short-term memory, accurately predict lake surface-water temperature (LSWT) using accessible air temperature data, aiding climate change research.
Area of Science:
- Environmental Science
- Climate Science
- Hydrology
Background:
- Lake surface-water temperature (LSWT) is crucial for lake ecosystems.
- The Arctic is warming faster than the global average, necessitating LSWT monitoring.
- Satellite and in-situ LSWT measurements in the Arctic are limited by cloud cover and accessibility.
Purpose of the Study:
- To develop and evaluate machine learning models for daily LSWT prediction in Arctic lakes.
- To utilize readily available air temperature data for LSWT modeling.
- To provide a reliable method for LSWT estimation in data-scarce Arctic regions.
Main Methods:
- Utilized historical data from 1960-2023 for Lake Inari.
- Developed four machine learning algorithms: Long-Short-Term Memory (LSTM), Support Vector Regression (SVR), Neural Network (NN), and Random Forest (RF).
- Validated model performance using coefficients of determination (R²).
Main Results:
- Both Arctic air temperature and LSWT showed significant warming trends (0.030 °C/yr and 0.023 °C/yr, respectively).
- The LSTM model demonstrated superior performance with R² values ranging from 0.96 to 0.98.
- SVR and NN models performed well, followed by the RF model.
Conclusions:
- Machine learning models, especially LSTM, can accurately predict Arctic LSWT using air temperature data.
- The developed models offer a viable solution for LSWT estimation in data-limited Arctic lakes.
- This approach supports climate change impact assessments in remote Arctic regions.
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