Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Author:

In Ieong Iek,Lou Inchio,Ung Wai Kin,Mok Kai Meng

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Reference27 articles.

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2. Camdevyren, H., Demyr, N., Kanik, A., & Keskyn, S. (2005). Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecological Modelling, 181, 581–589.

3. Zhang, W., Lou, I., Ung, W. K., Kong, Y., & Mok, K. M. (2013). Eutrophication analysis and principle component regression for two subtropical storage reservoirs in Macau. Desalination and Water Treatment, 51(37–39), 7331–7340.

4. Utojo, U. & Bakshi, B.R. (1995). Connection between neural networks and multivariate statistical methods: an overview. In: D.R. Baughman & Y.A. Liu (Eds) Neural Networks in Bioprocessing and Chemical Engineering. New York: Academic Press

5. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., & Aulagnier, S. (1996). Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52.

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