Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China
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DOI: 10.1371/journal.pone.0119082
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References listed on IDEAS
- Onderka, Milan, 2007. "Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)—A simple regression model," Ecological Modelling, Elsevier, vol. 209(2), pages 412-416.
- Liu, Yong & Guo, Huaicheng & Yang, Pingjian, 2010. "Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis," Ecological Modelling, Elsevier, vol. 221(4), pages 681-688.
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- Kim, Hyo Gyeom & Hong, Sungwon & Jeong, Kwang-Seuk & Kim, Dong-Kyun & Joo, Gea-Jae, 2019. "Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: Case study of Nakdong River," Ecological Modelling, Elsevier, vol. 398(C), pages 67-76.
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