Machine Learning in Extreme Value Analysis, an Approach to Detecting Harmful Algal Blooms with Long-Term Multisource Satellite Data

Long-term satellite observations have the ability to provide early warnings of harmful algal blooms (HABs). However, detecting HABs in optically complex coastal waters is somewhat challenging. In this article, we propose a two-step scheme, combining long short-term memory (LSTM) with extreme value analysis (EVA), for HAB detection. Essentially, the LSTM network builds a normal time series model on selected coordinate of long-term multisource satellite data. This model detects potential HAB dates by utilizing the LSTM predictive errors for an approximated Gaussian distribution. For each potential HAB date, the EVA approach then extracts the HAB distribution from the selected coordinate by considering the spatial correlation. A case study in Zhejiang coastal waters shows that our method exploits the advantages of both LSTM and EVA models, which not only has the strong prediction capability of LSTM for reducing HAB false alarm rate, but also achieves a dynamic HAB extraction through the EVA fitting.

Automated summary

This article introduces a two-step scheme that combines LSTM with EVA for detecting harmful algal blooms in optically complex coastal waters. By building a time series model and extreme value analysis, it improves the accuracy of HAB detection and dynamic extraction capabilities.