About this document
Deep Learning for Coastal Chlorophyll Forecast by mariana.m.alves is a document available to read on EtoBox.
This study focuses on optimizing a deep learning model, specifically a long short-term memory (LSTM) model, for forecasting chlorophyll a concentrations in Xiamen Bay to improve algal bloom predictions. The research demonstrates that using the change rate and relative change rate of chlorophyll a significantly enhances forecasting accuracy compared to traditional methods. The findings suggest that this approach can serve as an effective early warning system to mitigate the impacts of harmful algal blooms.
- Author
- mariana.m.alves
- Language
- EN