Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: Case study of Nakdong River
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DOI: 10.1016/j.ecolmodel.2019.02.003
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References listed on IDEAS
- Yu Liu & Du-Gang Xi & Zhao-Liang Li, 2015. "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," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
- Oh, Hee-Mock & Ahn, Chi-Yong & Lee, Jae-Won & Chon, Tae-Soo & Choi, Kyung Hee & Park, Young-Seuk, 2007. "Community patterning and identification of predominant factors in algal bloom in Daechung Reservoir (Korea) using artificial neural networks," Ecological Modelling, Elsevier, vol. 203(1), pages 109-118.
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Cited by:
- Wenxiang, Ding & Caiyun, Zhang & Shaoping, Shang & Xueding, Li, 2022. "Optimization of deep learning model for coastal chlorophyll a dynamic forecast," Ecological Modelling, Elsevier, vol. 467(C).
- Lu, Na & Niu, Jun & Kang, Shaozhong & Singh, Shailesh Kumar & Du, Taisheng, 2021. "A hybrid PCA-SEM-ANN model for the prediction of water use efficiency," Ecological Modelling, Elsevier, vol. 460(C).
- Xia, Rui & Zou, Lei & Zhang, Yuan & Zhang, Yongyong & Chen, Yan & Liu, Chengjian & Yang, Zhongwen & Ma, Shuqin, 2022. "Algal bloom prediction influenced by the Water Transfer Project in the Middle-lower Hanjiang River," Ecological Modelling, Elsevier, vol. 463(C).
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Keywords
Artificial neural network; Chlorophyll a; Sensitivity analysis; River modification; Algal blooms; Regulated river;All these keywords.
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