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AI-Powered Modeling and Forecasting of Harmful Algal Blooms Using Satellite Remote Sensing Data

Author(s): Shekhar Mahat; Zhiqiang Deng

Linked Author(s): Zhiqiang Deng

Keywords: Rtificial intelligence; Harmful algal blooms; Random forest; Satellite remote sensing

Abstract: Harmful Algal Blooms (HABs) and particularly toxic cyanobacterial harmful algal blooms (CyanoHABs) have become a growing threat globally to human and animal health. HABs often occur in large and shallow lakes like Lake Pontchartrain that is the second largest brackish estuary located in southeastern Louisiana, USA. The lake has been negatively impacted by the frequent occurrence of CyanoHABs over the past decades. Satellite remote sensing and particularly Artificial Intelligence (AI) -powered remote sensing provides a promising technology for modeling and forecasting of HABs in Lake Pontchartrain and beyond. The aim of this work is to forecast the HAB concentration in Lake Pontchartrain by synergistically combining the modeling power of AI technology and the high resolution data from satellite remote sensing technology.

DOI:

Year: 2024

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