Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes
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- Vanya Van Belle & Ben Van Calster & Sabine Van Huffel & Johan A K Suykens & Paulo Lisboa, 2016. "Explaining Support Vector Machines: A Color Based Nomogram," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-33, October.
- Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LP, vol. 16(4), pages 917-937, December.
- Yuting Bai & Xuebo Jin & Xiaoyi Wang & Tingli Su & Jianlei Kong & Yutian Lu, 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series," Complexity, Hindawi, vol. 2019, pages 1-11, September.
- Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
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Keywords
algal bloom; sustainable governance; group decision-making; machine learning;All these keywords.
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