Abstract
Temperature, salinity, and geographic locations are three important factors while determining thermocline. We mainly focus on analyzing how these factors affect the formation of thermocline using machine learning methods. An improvement based on ‘entropy value method’ while choosing thermocline is demonstrated in the paper. The experiments adopt Argo data sets and the experimental results show that machine learning methods can compute thermocline and related data effectively.
This work was supported in part from the National Natural Science Foundation of China (51409117, 51679105).
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Hu, C., Gou, Y., Zhang, T., Wang, K., He, L., Jiang, Y. (2018). Thermocline Analysis Based on Entropy Value Methods. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_55
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DOI: https://doi.org/10.1007/978-981-10-7605-3_55
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