Computer Prediction of Seawater Sensor Parameters in the Central Arctic Region Based on Hybrid Machine Learning Algorithms.
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In recent years, with the large-scale reduction of Arctic sea ice, the supplement of chlorophyll sensor data in seawater has become an essential part of environmental assessment. Accurately predicting the chlorophyll sensor data in seawater is of great significance to protect the Arctic marine ecological environment. A machine learning prediction method combined with wavelet transform is proposed. This process uses data from upper ocean observation buoys placed in the Arctic Ocean (A.O.) to predict the sensor analogue of chlorophyll-a (C.A.) in the upper ocean of the Arctic Ocean. Choose the best wavelet transform method and prevent the LSTM gradient from disappearing. A model combining SAE (stacked autoencoder) Bi (bidirectional) LSTM (long short-term memory) and wavelet transform is proposed. Experiments were conducted to compare the predictive performance of buoy data input as univariate at two different times and locations in the Arctic Ocean. The results show that compared with ot.....
Sustainable Development Goals (SDG)14.1
Spatial CoverageArctic Ocean
CitationWang, Y., Guo, J., Yang, Z., Dou, Y., Chang, X., et al.(2020) Computer Prediction of Seawater Sensor Parameters in the Central Arctic Region Based on Hybrid Machine Learning Algorithms. IEEE Access, 8:9261369, pp.213783–213798. DOI: https://doi.org/10.1109/ACCESS.2020.3038570
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