Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction
Author:
Publisher
ASTES Journal
Subject
Management of Technology and Innovation,Physics and Astronomy (miscellaneous),Engineering (miscellaneous)
Link
https://www.astesj.com/publications/ASTESJ_020635.pdf
Reference13 articles.
1. Ruijian Zhang, "Applying Parallel Programming and High Performance Computing to Speed up Data Mining Processing", Proceedings of the 16th IEEE international Conference on Computer and Information Science, May 2017.
2. Faruk, D. "A hybrid neural network and ARIMA model for water quality time series prediction" Appl. Aritf. Intel., 23, 586-594, 2009. https://doi.org/10.1016/j.engappai.2009.09.015
3. M. He, Z. L. He, "Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California", Water. Res., 42, 2563-2573, 2008. https://doi.org/10.1016/j.watres.2008.01.002
4. Labiosa, J. Leckie, R. Shachter, D. Freyberg, J. Rytuba, "Incorporating Uncertainty in Watershed Management Decision-Making: A Mercury TMDL Case Study" in Watershed Management Conference, Williamsburg, Virginia, United States, 2005. https://doi.org/10.1061/40763(178)125
5. Said, "The Implementation of a Bayesian Network for Watershed Management Decisions" Int. Ser. Prog. Wat. Res., 20, 591-605. 2005. https://doi.org/10.1007/s11269-006-3088-z
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