A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model
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 o...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9504580/ |
| _version_ | 1819147740497575936 |
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| author | Nur Aqilah Paskhal Rostam Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Abdul Latif Ahmad Boon Seng Ooi Derek Juinn Chieh Chan |
| author_facet | Nur Aqilah Paskhal Rostam Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Abdul Latif Ahmad Boon Seng Ooi Derek Juinn Chieh Chan |
| author_sort | Nur Aqilah Paskhal Rostam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T13:34:37Z |
| format | Article |
| id | doaj.art-057c5575ff7442c0b62ba1749f8acdb7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T13:34:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-057c5575ff7442c0b62ba1749f8acdb72022-12-21T18:24:04ZengIEEEIEEE Access2169-35362021-01-01910824910826510.1109/ACCESS.2021.31020449504580A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive ModelNur Aqilah Paskhal Rostam0Nurul Hashimah Ahamed Hassain Malim1https://orcid.org/0000-0002-5652-9152Rosni Abdullah2https://orcid.org/0000-0002-3061-5837Abdul Latif Ahmad3Boon Seng Ooi4Derek Juinn Chieh Chan5https://orcid.org/0000-0002-1046-0364School of Computer Sciences, Universiti Sains Malaysia, USM, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, USM, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, USM, Penang, MalaysiaSchool of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaSchool of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaSchool of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaAn 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.https://ieeexplore.ieee.org/document/9504580/Deep learningharmful algal bloom (HAB)IoTlong short-term memory (LSTM)machine learningtime series forecasting |
| spellingShingle | Nur Aqilah Paskhal Rostam Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Abdul Latif Ahmad Boon Seng Ooi Derek Juinn Chieh Chan A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model IEEE Access Deep learning harmful algal bloom (HAB) IoT long short-term memory (LSTM) machine learning time series forecasting |
| title | A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model |
| title_full | A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model |
| title_fullStr | A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model |
| title_full_unstemmed | A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model |
| title_short | A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model |
| title_sort | complete proposed framework for coastal water quality monitoring system with algae predictive model |
| topic | Deep learning harmful algal bloom (HAB) IoT long short-term memory (LSTM) machine learning time series forecasting |
| url | https://ieeexplore.ieee.org/document/9504580/ |
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