A novel single-parameter approach for forecasting algal blooms

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• 作者：Xi Xiao^a ; ^b ; ^c ; Junyu He^a ; ^b ; ^c ; Haomin Huang^b ; ^c ; ^d ; Todd R. Miller^e ; George Christakos^a ; Elke S. Reichwaldt^f ; Anas Ghadouani^f ; Shengpan Lin^g ; Xinhua Xu^b ; Jiyan Shi^b ; ^c ; ^{shijiyan@zju.edu.cn}
• 关键词：Artificial neural network ; Forecasting ; Harmful algal bloom ; Phytoplankton ; Single-parameter ; Wavelet analysis
• 刊名：Water Research
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：108
• 期：Complete
• 页码：222-231
• 全文大小：2528 K
• 卷排序：108

文摘

A single-parameter approach for the forecasting of harmful algal bloom is presented. This approach combines wavelet analysis and artificial neural network. The approach was verified by the datasets of algal density from China and the US. Compared to common algal forecasting methods, the approach showed greater accuracy. This novel approach can save 85% of the cost in building HAB monitoring system.