Harmful Algae Blooms (HABs) have emerged as a critical environmental concern, causing significant damage to marine ecosystems and coastal communities worldwide. Sabah, a coastal region in Malaysia, has experienced increased frequency and intensity of HAB events, leading to severe ecological and economic consequences. Traditional methods for detecting and predicting HABs are limited by their manual and time-consuming nature, hindering timely responses and effective mitigation strategies. To address this issue, this study presents an approach for predicting HABs in Sabah using a Deep Learning model. The proposed deep learning model harnesses the potential of artificial intelligence by integrating historical HAB occurrence records. In the first phase, the researcher collected and organized data from the Sabah Fisheries Department, then carefully cleaned and transformed it into a more practical format. After reviewing existing research, the study chose two promising techniques: the Artificial Neural Networks (ANNs) algorithm and the Long Short-Term Memory (LSTM). These techniques were experimented with to determine the most effective one for predicting HABs. Phase three involved rigorous testing of the prediction model using existing data, providing insights into how well it functions with the dataset and revealing the results obtained. In Phase four, the focus shifted to evaluating the model’s performance, specifically identifying any reduced accuracy or shortcomings in the prediction percentages. This study adopts various evaluation metrics to assess the model’s performance, such as accuracy, precision, recall, and F1-score, on a dataset with distinct training, validation, and testing sets. The authors also conducted usability testing to gather users’ feedback on the proposed system. By providing timely predictions, the proposed model empowers stakeholders to take proactive measures to mitigate the ecological and socio-economic impacts of HABs on the marine ecosystem and coastal communities in Sabah. The findings will contribute substantially to the community by improving early warning systems and optimizing resource allocation for monitoring and controlling HABs.

1.
Teen,
LiM
Po
,
U. S. U. P.
GireS
, and
LeAW CHUi
Pin
.
"Harmful algal blooms in Malaysian waters
."
Sains Malaysiana
41
, no.
12
(
2012
):
1509
1515
.
2.
Li
,
Peiyao
,
Ye
Yao
,
Jijian
Lian
, and
Chao
Ma
.
"Effect of thermal stratified flow on algal blooms in a tributary bay of the Three Gorges reservoir
."
Journal of Hydrology
601
(
2021
):
126648
.
3.
Shen
,
Li
,
Huiping
Xu
, and
Xulin
Guo
.
"Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework
."
Sensors
12
, no.
6
(
2012
):
7778
7803
.
4.
Anderson
,
Clarissa
R.
,
Stephanie K.
Moore
,
Michelle C.
Tomlinson
,
Joe
Silke
, and
Caroline K.
Cusack
. "Living with harmful algal blooms in a changing world: strategies for modeling and mitigating their effects in coastal marine ecosystems." In
Coastal and marine hazards, risks, and disasters
, pp.
495
561
.
Elsevier
,
2015
.
5.
Aleynik
,
Dmitry
,
Andrew C.
Dale
,
Marie
Porter
, and
Keith
Davidson
.
"A high resolution hydrodynamic model system suitable for novel harmful algal bloom modelling in areas of complex coastline and topography
."
Harmful algae
53
(
2016
):
102
117
.
6.
Wong
,
Ken
T.M.
,
Joseph HW
Lee
, and
I. J.
Hodgkiss
.
"A simple model for forecast of coastal algal blooms
."
Estuarine, Coastal and Shelf Science
74
, no.
1-2
(
2007
):
175
196
.
7.
Li
,
Xiu
,
Jin
Yu
,
Zhuo
Jia
, and
Jingdong
Song
. "Harmful algal blooms prediction with machine learning models in Tolo Harbour." In
2014 International conference on smart computing
, pp.
245
250
.
IEEE
,
2014
.
8.
Hill
,
Paul
R.
,
Anurag
Kumar
,
Marouane
Temimi
, and
David R.
Bull
.
"HABNet: Machine learning, remote sensing-based detection of harmful algal blooms
."
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
13
(
2020
):
3229
3239
.
9.
McLellan
,
Sam
,
Andrew
Muddimer
, and
S. Camille
Peres
.
"The effect of experience on system usability scale ratings
."
Journal of usability studies
7
, no.
2
(
2012
):
56
67
.
10.
Korstanje
,
Joos
.
Advanced forecasting with Python
.
United States
:
Apress
,
2021
.
This content is only available via PDF.
You do not currently have access to this content.