Prediction of Harmful Algal Blooms Severity Using Machine Learning and Deep Learning Techniques

Author:

Karthikeyan N.,Bhargav M.,krishna S. Hari,Madhav Y. Sai,Sajana T.

Publisher

Springer Nature Singapore

Reference28 articles.

1. Bretz CK, Manouki TJ, Kvitek RG (2002) Emerita analoga (Stimpson) as an indicator species for paralytic shellfish poisoning toxicity along the California coast. Toxicon 40(8):1189–1196

2. Anderson DM, Fensin E, Gobler CJ, Hoeglund AE, Hubbard KA, Kulis DM, Landsberg JH et al. (2021) Marine harmful algal blooms (HABs) in the United States: history, current status and future trends. Harmful Algae 102:101975

3. Ghatkar JG, Singh RK, Shanmugam P (2019) Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model. Int J Remote Sens 40(24):9412–9438

4. DrivenData runs online machine learning competitions with social impact and works directly with mission-driven organizations to drive change through data science and engineering. It provides the datasets for the problems. Tick Tick Bloom competion https://drivendata.co/blog/tick-tick-bloom-benchmark

5. Image courtesy of NASA Earth Observatory, Joshua Stevens, using Landsat imagery from NASA/USGS. Depicts a 2017 algal bloom in Lake Erie., Tick Tick Bloom: Harmful Algal Bloom Detection Challenge

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