Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data
Author:
Funder
Institute for Information and Communications Technology Promotion
Ministry of Science, ICT and Future Planning
Publisher
Elsevier BV
Reference58 articles.
1. Predicting cyanobacterial harmful algal blooms (CyanoHABs) in a regulated river using a revised EFDC model;Ahn;Water,2021
2. Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams;Ather;J. Hydrol.,2023
3. Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network;Awad;Ecol. Inf.,2014
4. Phytoplankton bloom status: chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA;Boyer;Ecol. Indicat.,2009
5. Paradox versus paradigm: a disconnect between understanding and management of freshwater cyanobacterial harmful algae blooms;Bramburger;Freshw. Biol.,2023
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