Applying Neural Networks to Predict Factors Affecting Harmful Algal Blooms for Timely Alerting and Implementing Preventive Measures in Ireland's Marine Ecosystem.

Author

Nikolai Potapov, CCT College Dublin

Supervisor

Vikas Tomer

Programme

MSc in Data Analytics

Abstract

This study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.

Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems.

Date of Award

2024

Full Publication Date

2024

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

Recommended Citation

Potapov, Nikolai, "Applying Neural Networks to Predict Factors Affecting Harmful Algal Blooms for Timely Alerting and Implementing Preventive Measures in Ireland's Marine Ecosystem." (2024). ICT. 54.
https://arc.cct.ie/ict/54