A complete proposed framework for coastal water quality monitoring system with algae predictive model
Open accessAbstract
An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chl-a) as a strong indicator of algal presence for coastal studies.
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Algal digital twin has progressed and expanded in tandem with AI, IoT, and big data in the algal cultivation for biomass production sector to sustain the environment.
“Correspondingly, ADT has also progressed and expanded in tandem with AI, IoT, and BD in the ACB sector to sustain the environment [1,11 -14].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [14]
The Introduction describes combining IoT with predictive modelling for coastal algal growth, and the Data-Driven Algal Growth Prediction Methods section discusses AI-based approaches. But the paper concerns coastal water-quality monitoring and harmful-bloom prediction; these passages do not support the claim that algal digital twins have expanded alongside big data in cultivation for biomass production. (LLM, conf=0.84)
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Sheik, A. G., Kumar, A., Ansari, F. A., Raj, V., Peleato, N. M., Patan, A. K., Kumari, S., & Bux, F. (2024) · Algal Research · doi:10.1016/j.algal.2024.103779 · Cited by 37 · see citing works
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- Authors
- Rostam, N. A. P., Malim, N. H. A. H., Abdullah, R., Ahmad, A. L., Ooi, B. S., & Chan, D. J. C.
- Affiliations
- Universiti Sains Malaysia (MY)
- ORCID
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 108249-108265
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3192711867
- Cited by
- 55
- References
- 81
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 12 · 2023: 13 · 2022: 4
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Malaysian Ministry of Higher Education through the Transdisciplinary Research Grant (TRGS/1/2018/USM/01/5/4-203.PKOMP.67612)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- harmful algal blooms, algal bloom prediction, coastal water quality monitoring, IoT sensors, sensor integration, chlorophyll a, LSTM, feature selection, water pollution prevention
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