Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10661-023-11276-9.pdf
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2. Aljanabi, Q. A., Chik, Z., Allawi, M. F., El-Shafie, A. H., Ahmed, A. N., & El-Shafie, A. (2018). Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment. Neural Computing and Applications, 30(8), 2459–2469. https://doi.org/10.1007/s00521-016-2807-5
3. Azimi-Pour, M., Eskandari-Naddaf, H., & Pakzad, A. (2020). Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construction and Building Materials, 230. https://doi.org/10.1016/j.conbuildmat.2019.117021
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5. Chen, B., Liu, H., Xiao, W., Wang, L., & Huang, B. (2020). A machine-learning approach to modeling picophytoplankton abundances in the South China Sea. Progress in Oceanography, 189, 102456. https://doi.org/10.1016/j.pocean.2020.102456
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