Machine learning in extreme value analysis, an approach to detecting harmful algal blooms with long-term multisource satellite data

HIGHLIGHTS

• who: Weiwen Ye et al. from the School of Earth Sciences, Zhejiang University, Zheda Road, Hangzhou, China have published the research work: Machine Learning in Extreme Value Analysis, an Approach to Detecting Harmful Algal Blooms with Long-Term Multisource Satellite Data, in the Journal: (JOURNAL) of 31/08/2019
• what: The authors propose a two-step scheme combining long short-term memory (LSTM) with extreme value analysis (EVA) for HAB detection. The authors adopt a factorized spatiotemporal modeling to condition incomplete data samples for HAB detection, while integrating domain knowledge by assessing spatial correlations and . . .
  

   

  

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