Prediction of harmful algae blooms in Sabah using deep learning model

Mohd Firdaus Patitingi (2022) Prediction of harmful algae blooms in Sabah using deep learning model. Universiti Malaysia Sabah. (Unpublished)

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Abstract

Harmful algal blooms (HAB) incident have been increasingly reported in the country and it became a critical global environmental concern which might put economic development and sustainability at risk. However, the analysis and accurate prediction of algae blooms remains a challenging scientific problem. In this project, a method based on deep learning is an approach to analysis and predict highly dynamic to the incident of HAB. People expect that such a system will significantly facilitate researchers, local administrators and civilians in monitoring water bodies and immediately solve any excessive algae growth. From the results of this study, it can be proven that the deep learning model make a better generalization and greater accuracy in predicting algae blooms than a traditional shallow neural network does.

Item Type:	Academic Exercise
Keyword:	Algae , Deep learning model , Harmful algal blooms
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) S Agriculture > SH Aquaculture. Fisheries. Angling > SH1-691 Aquaculture. Fisheries. Angling
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	18 Jul 2022 11:29
Last Modified:	25 Jan 2024 16:27
URI:	https://eprints.ums.edu.my/id/eprint/33203

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