Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives – A short review
Abstract
Machine learning (ML) models are widely used methods for analyzing data from sensors and satellites to monitor climate change, predict natural disasters, and protect wildlife. However, the application of these technologies for monitoring and managing algal blooms in freshwater environments is relatively new and novel. The commonly used models in algal blooms (ABS) so far are artificial neural networks (ANN), random forests (RF), support vector machine (SVM), data-driven modeling, and long short-term memory (LSTM). In the past, researchers have mostly worked on predicting the effluent parameters, nutrients, microculture, area and weather conditions, meteorological factors, ground waters, energy optimization, and metallic substances in algal blooms using ML models. Most of the studies have employed performance metrics like root mean squared error, mean squared error, peak signal, precision, and determination coefficient as their primary model performance measures for accuracy analysis, and the usage of transfer, and activation function. While there have been some studies on this topic, several research gaps are still to be addressed. The most significant gaps are related to the limited application of ML in different algae bloom scenarios, the interpretability of ML models, and the lack of integration with existing monitoring systems. Keeping these in mind, this review article has been methodically arranged to present an overview of the past studies, their limitations, and the w…
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An algal digital twin, as an integrated practice, helps to drive data-driven models and ecological sustainability initiatives using big data, IoT, AI and ML, and advanced computing for information gathering and analysis.
“An ADT, as an integrated practice, helps to drive data-driven (DD) models, and ecological sustainability initiatives [15,16] using BD, IoT, AI & ML, and advanced computing for informa tion gathering as well as analysis [17,18].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
There is no clear idea of what digital twin models involve or what kinds of systems they duplicate.
“There is no clear idea of what these digital twin models (DTM) involve or what kinds of systems they duplicate [2,18,20].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
As of now, neither advanced data processing computers for algal digital twins nor a standard framework for cutting-edge computing exists.
“As of now, neither advanced data processing computers for ADTs [18,50] nor a standard framework for cutting-edge computing exists.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
There is a stated shortage of data on public, financial, and environmental issues, which restricts the accuracy and realism of algal digital twins.
“There is a stated shortage of data on public, financial, and environmental issues, which restricts the accuracy and realism of ADTs [18].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
Storing a large dataset in a local data center is timely and inadequate.
“[18] reported that storing a large dataset in a local data center is timely and inadequate.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
Simulation models are crucial for decision-making tools such as ADT or AI-AIoT.
“Simulation models are crucial for decision-making tools like ADT or AI-AIoT [18].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [18]
Cites
1 retracted
Hilal, A. M., Althobaiti, M. M., Eisa, T. A. E., Alabdan, R., Hamza, M. A., Motwakel, A., Duhayyim, M. A., & Negm, N. (2022) · Adsorption Science & Technology · doi:10.1155/2022/8448489 · Cited by 23 · see citing works
RetractedHarmful algal blooms and climate change: learning from the past and present to forecast the future
Wells, M. L., Trainer, V. L., Smayda, T. J., Karlson, B. S. O., Trick, C. G., Kudela, R. M., Ishikawa, A., Bernard, S., Wulff, A., Anderson, D. M., et al. (2015) · Harmful Algae · doi:10.1016/j.hal.2015.07.009 · Cited by 828 · see citing works
Hao, X., Li, Y., Ren, S., Wu, H., & Hao, Y. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116504 · Cited by 767 · see citing works
Interpretable Machine Learning
Molnar, C. (2020) · Cited by 506 · see citing works
Data-driven performance analyses of wastewater treatment plants: A review
Newhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019) · Water Research · doi:10.1016/j.watres.2019.03.030 · Cited by 489 · see citing works
Zhao, L., Dai, T., Qiao, Z. F., Sun, P., Hao, J., & Yang, Y. (2019) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2019.11.014 · Cited by 455 · see citing works
A review of remote sensing applications for water security: Quantity, quality, and extremes
Chawla, I., Karthikeyan, L., & Mishra, A. K. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124826 · Cited by 376 · see citing works
Jawad, J., Hawari, A. H., & Zaidi, S. M. J. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.129540 · Cited by 360 · see citing works
Recent advances in sensor fault diagnosis: a review
Li, D., Wang, Y., Wang, J., Wang, C., & Duan, Y. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.111990 · Cited by 323 · see citing works
Application of machine learning in anaerobic digestion: Perspectives and challenges
Cruz, I. A., Chuenchart, W., Long, F., Surendra, K. C., Andrade, L. R. S., Bilal, M., Liu, H., Figueiredo, R. T., Khanal, S. K., & Ferreira, L. F. R. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126433 · Cited by 284 · see citing works
Bhagat, S. K., Tung, T. M., & Yaseen, Z. M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119473 · Cited by 252 · see citing works
Park, J., Lee, W. H., Kim, K. T., Park, C. Y., Lee, S., & Heo, T.-Y. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155070 · Cited by 243 · see citing works
Fenu, A., Guglielmi, G., Jimenez, J., Spérandio, M., Saroj, D. P., Lesjean, B., Brepols, C., Thoeye, C., & Nopens, I. (2010) · Water Research · doi:10.1016/j.watres.2010.06.007 · Cited by 223 · see citing works
Lee, S. M., & Lee, D.-H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15071322 · Cited by 208 · see citing works
Singh, N. K., Yadav, M. K., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S. K., & Show, P. L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128486 · Cited by 195 · see citing works
Ensafi, A. A., Khayamian, T., Benvidi, A., & Mirmomtaz, E. (2006) · Analytica Chimica Acta · doi:10.1016/j.aca.2006.01.015 · Cited by 188 · see citing works
Baig, M. F., Mustafa, M. R. U., Baig, I., Takaijudin, H. B., & Zeshan, M. T. (2022) · Water · doi:10.3390/w14030402 · Cited by 179 · see citing works
Algicidal Bacteria: A Review of Current Knowledge and Applications to Control Harmful Algal Blooms
Coyne, K. J., Wang, Y., & Johnson, G. L. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.871177 · Cited by 173 · see citing works
Sundui, B., Calderon, O. A. R., Abdeldayem, O. M., Gil, J. L., Rene, E. R., & Sambuu, U. (2021) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-020-01993-x · Cited by 169 · see citing works
A deep learning approach for fault diagnosis of induction motors in manufacturing
Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017) · Chinese Journal of Mechanical Engineering · doi:10.1007/s10033-017-0189-y · Cited by 167 · see citing works
Yaqub, M., Asif, H., Kim, S., & Lee, W. (2020) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2020.101388 · Cited by 155 · see citing works
Taoufik, N., Boumya, W., Achak, M., Chennouk, H., Dewil, R., & Barka, N. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.150554 · Cited by 155 · see citing works
Rebooting data-driven soft-sensors in process industries: A review of kernel methods
Liu, Y., & Xie, M. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.03.012 · Cited by 145 · see citing works
HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms
Hill, P. R., Kumar, A., Temimi, M., & Bull, D. R. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3001445 · Cited by 143 · see citing works
Self-citationHosseinzadeh, A., Baziar, M., Alidadi, H., Zhou, J. L., Altaee, A., Najafpoor, A. A., & Jafarpour, S. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.122926 · Cited by 136 · see citing works
Adaptive kernel density-based anomaly detection for nonlinear systems
Zhang, L., Lin, J., & Karim, R. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.10.009 · Cited by 136 · see citing works
Shi, X., Qi, M., Tang, H., & Han, X. (2015) · Estuarine Coastal and Shelf Science · doi:10.1016/j.ecss.2015.02.007 · Cited by 134 · see citing works
Heavy metal resistance in algae and its application for metal nanoparticle synthesis
Priyadarshini, E., Priyadarshini, S. S., & Pradhan, N. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09685-3 · Cited by 131 · see citing works
Cruz, R. C., Costa, P. R., Vinga, S., Krippahl, L., & Lopes, M. B. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9030283 · Cited by 131 · see citing works
Kim, J. H., Shin, J.-K., Lee, H., Lee, D. H., Kang, J., Cho, K. H., Lee, Y.-G., Chon, K., Baek, S., & Park, Y. (2021) · Water Research · doi:10.1016/j.watres.2021.117821 · Cited by 125 · see citing works
Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models
Zheng, L., Wang, H., Liu, C., Zhang, S., Ding, A., Xie, E., Li, J., & Wang, S. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.113060 · Cited by 121 · see citing works
Hwangbo, S., Al, R., Chen, X., & Sin, G. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c05231 · Cited by 121 · see citing works
Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods
Shin, Y., Kim, T., Hong, S., Lee, S., Lee, S., Hong, S., Lee, C., Kim, T., 이창식, Park, J., et al. (2020) · Water · doi:10.3390/w12061822 · Cited by 111 · see citing works
Ke, B., Nguyen, H., Bui, X.-N., Bac, B. H., Choi, Y., Zhou, J., Moayedi, H., Costache, R. D., & Trang, T. N. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.130204 · Cited by 110 · see citing works
Predicting coastal algal blooms with environmental factors by machine learning methods
Yu, P., Gao, R., Zhang, D., & Liu, Z. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2020.107334 · Cited by 110 · see citing works
Abduljabbar, R. L., Dia, H., & Tsai, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-03282-z · Cited by 106 · see citing works
Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data
Nourani, V., Asghari, P., & Sharghi, E. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125772 · Cited by 104 · see citing works
Matheri, A. N., Ntuli, F., Ngila, J. C., Seodigeng, T. G., & Zvinowanda, C. M. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107308 · Cited by 102 · see citing works
Hosseinzadeh, A., Zhou, J. L., Altaee, A., Baziar, M., & Li, X. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123391 · Cited by 95 · see citing works
Khatri, N., Khatri, K. K., & Sharma, A. (2020) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2020.101477 · Cited by 95 · see citing works
Yang, Z., Wei, C., Liu, D., Lin, Q., Huang, Y., Wang, C., Ji, D., Ma, J., & Yang, H. (2022) · Water Research · doi:10.1016/j.watres.2021.118030 · Cited by 94 · see citing works
Viet, N. D., Jang, D., Yoon, Y., & Jang, A. (2021) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2021.1940031 · Cited by 93 · see citing works
A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom
Izadi, M., Sultan, M., Kadiri, R. E., Ghannadi, M. A., & Abdelmohsen, K. (2021) · Remote Sensing · doi:10.3390/rs13193863 · Cited by 91 · see citing works
Park, Y., Lee, H. K., Shin, J.-K., Chon, K., Kim, S., Cho, K. H., Kim, J. H., & Baek, S. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112415 · Cited by 90 · see citing works
Liu, Y., Ramin, P., Flores‐Alsina, X., & Gernaey, K. V. (2023) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2023.02.043 · Cited by 89 · see citing works
Therrien, J.-D., Nicolaï, N., & Vanrolleghem, P. A. (2020) · Water Science & Technology · doi:10.2166/wst.2020.393 · Cited by 87 · see citing works
Alsulaili, A. D., & Refaie, A. (2020) · Water Science & Technology Water Supply · doi:10.2166/ws.2020.199 · Cited by 85 · see citing works
Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics
Baracchini, T., Wüest, A., & Bouffard, D. (2020) · Water Research · doi:10.1016/j.watres.2020.115529 · Cited by 83 · see citing works
Predicting algal blooms: Are we overlooking groundwater?
Brookfield, A. E., Hansen, A. T., Sullivan, P., Czuba, J. A., Kirk, M. F., Li, L., Newcomer, M., & Wilkinson, G. M. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144442 · Cited by 81 · see citing works
Shahbeik, H., Shafizadeh, A., Nadian, M. H., Jeddi, D., Mirjalili, S., Yang, Y., Lam, S. S., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.135881 · Cited by 79 · see citing works
Picos-Benítez, A. R., Martínez-Vargas, B. L., Durón-Torres, S. M., Brillas, E., & Peralta-Hernández, J. M. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2020.06.020 · Cited by 78 · see citing works
Digital transformation in water organizations
Boyle, C., Ryan, G., Bhandari, P., Law, K. M. Y., Gong, J., & Creighton, D. (2022) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001555 · Cited by 70 · see citing works
Exploring algal technologies for a circular bio-based economy in rural sector
Vishwakarma, R., Dhaka, V., Ariyadasa, T. U., & Malik, A. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131653 · Cited by 70 · see citing works
Taming the waves: sine as activation function in deep neural networks
Parascandolo, G., Huttunen, H. J., & Virtanen, T. I. (2017) · Cited by 68 · see citing works
Liu, H., Zhang, Y., & Zhang, H. (2020) · Process Biochemistry · doi:10.1016/j.procbio.2020.06.020 · Cited by 65 · see citing works
Miao, S. J., Zhou, C., AlQahtani, S. A., Alrashoud, M. R., Ghoneim, A., & Lv, Z. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103009 · Cited by 64 · see citing works
Applications of IoT and Artificial Intelligence in Water Quality Monitoring and Prediction: A Review
Mustafa, H. M., Mustapha, A., Hayder, G., & Salisu, A. (2021) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358675 · Cited by 64 · see citing works
Machine learning based soft sensor model for BOD estimation using intelligence at edge
Pattnaik, B. S., Pattanayak, A. S., Udgata, S. K., & Panda, A. K. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00259-9 · Cited by 61 · see citing works
Shen, J., Qin, Q., Wang, Y., & Sisson, M. (2019) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.02.005 · Cited by 61 · see citing works
Differential feature based hierarchical PCA fault detection method for dynamic fault
Zhou, F., Park, J. H., & Liu, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.007 · Cited by 58 · see citing works
Brackish water desalination in RO–single pass EDR system
Turek, M., Was, J., & Dydo, P. (2009) · Desalination and Water Treatment · doi:10.5004/dwt.2009.710 · Cited by 58 · see citing works
Chlorophyll soft-sensor based on machine learning models for algal bloom predictions
Mozó, A., Morón-López, J., Vakaruk, S., Pompa-Pernía, Á. G., González-Prieto, Á., Aguilar, J. A. P., Gómez-Canaval, S., & Ortiz, J. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-17299-5 · Cited by 56 · see citing works
Integrated explainable deep learning prediction of harmful algal blooms
Lee, D.-H., Kim, M., Lee, B., Chae, S., Kwon, S., & Kang, S. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122046 · Cited by 55 · see citing works
Bao, T., Fu, R., Jiang, Y., Wen, W., Zhang, X., & Wang, S. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c02125 · Cited by 54 · see citing works
Wang, Q., Sun, L., Zhu, Y., Wang, S., Duan, C., Yang, C., Zhang, Y., Liu, D., Zhao, L., & Tang, J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152558 · Cited by 53 · see citing works
Spatial–temporal variations in urbanization in Kunming and their impact on urban lake water quality
Yang, K., Luo, Y., Chen, K., Yang, Y., Shang, C., Yu, Z., Xu, J., & Zhao, Y. (2020) · Land Degradation and Development · doi:10.1002/ldr.3543 · Cited by 52 · see citing works
Interpretable machine learning
Molnar, C. (2020) · doi:10.58248/pn633 · Cited by 51 · see citing works
Harmful algal bloom warning based on machine learning in maritime site monitoring
Wen, J., Yang, J., Li, Y., & Gao, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108569 · Cited by 51 · see citing works
Machine learning for anomaly detection in cyanobacterial fluorescence signals
Almuhtaram, H., Zamyadi, A., & Hofmann, R. (2021) · Water Research · doi:10.1016/j.watres.2021.117073 · Cited by 49 · see citing works
Eleonora, V., Ricci, F., Capellacci, S., Casabianca, S., Scardi, M., & Penna, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-40664-w · Cited by 49 · see citing works
Jauzein, C., Açaf, L., Accoroni, S., Asnaghi, V., Fricke, A., Hachani, M. A., Saab, M. A., Chiantore, M., Mangialajo, L., Totti, C., et al. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.03.089 · Cited by 49 · see citing works
Mathai, P. P., Staley, C. M., & Sadowsky, M. J. (2020) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2020.106050 · Cited by 47 · see citing works
Chhipi-Shrestha, G., Mian, H. R., Mohammadiun, S., Rodriguez, M., Hewage, K., & Sadiq, R. (2023) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-023-02477-4 · Cited by 46 · see citing works
Monitoring of wastewater treatment processes using dynamic concurrent kernel partial least squares
Liu, H., Yang, J., Zhang, Y., & Yang, C. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2020.09.034 · Cited by 45 · see citing works
Machine learning-based prediction of harmful algal blooms in water supply reservoirs
Jeong, B., Chapeta, M. R., Kim, M., Kim, J., Shin, J., & Cha, Y. (2022) · Water Quality Research Journal · doi:10.2166/wqrj.2022.019 · Cited by 43 · see citing works
Jung, S., Yoo, C., & Im, J. (2022) · Remote Sensing · doi:10.3390/rs14030575 · Cited by 42 · see citing works
Machine learning approach towards explaining water quality dynamics in an urbanised river
Schäfer, B., Beck, C., Rhys, H. I., Soteriou, H., Jennings, P., Beechey, A., & Heppell, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-16342-9 · Cited by 41 · see citing works
Digital Twins for Water Distribution Systems
Berglund, E. Z., Shafiee, M. E., Xing, L., & Wen, J. (2023) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-5786 · Cited by 41 · see citing works
Lin, S., Pierson, D. C., & Mesman, J. P. (2023) · Geoscientific model development · doi:10.5194/gmd-16-35-2023 · Cited by 40 · see citing works
Tang, Y., Feng, Y., Fung, S., Xomchuk, V. R., Jiang, M., Moore, T., & Beckler, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3208620 · Cited by 37 · see citing works
Groundwater Level Monitoring Networks Design with Machine Learning Methods
Teimoori, S., Olya, M. H., & Miller, C. J. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130145 · Cited by 37 · see citing works
Mansoori, A., Zeinalnezhad, M., & Nazarimanesh, L. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/9673395 · Cited by 36 · see citing works
Zhou, J., Huang, F., Shen, W., Liu, Z., Corriou, J., & Seferlis, P. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2020.08.032 · Cited by 36 · see citing works
Mokhtari, H. A., Bagheri, M., Mirbagheri, S. A., & Akbari, A. (2020) · Water and Environment Journal · doi:10.1111/wej.12565 · Cited by 34 · see citing works
Krishnaraj, A., & Deka, P. C. (2020) · Environmental Monitoring and Assessment · doi:10.1007/s10661-020-08624-4 · Cited by 34 · see citing works
Towards a Taxonomy for the Opacity of AI Systems
Facchini, A., & Termine, A. (2022) · Studies in Applied Philosophy, Epistemology and Rational Ethics · doi:10.1007/978-3-031-09153-7_7 · Cited by 34 · see citing works
Harley, J. R., Lanphier, K., Kennedy, E. G., Whitehead, C., & Bidlack, A. L. (2020) · Harmful Algae · doi:10.1016/j.hal.2020.101918 · Cited by 33 · see citing works
Aka, R. J. N., Wu, S. X., Mohotti, D. G., Bashir, M. A., & Nasir, A. (2022) · Water Research · doi:10.1016/j.watres.2022.119107 · Cited by 32 · see citing works
Modeling of Bunus regional sewage treatment plant using machine learning approaches
Pham, Q. B., Gaya, M. S., Abba, S. I., Abdulkadir, R. A., Esmaılı, P., Linh, N. T. T., Sharma, C., Malik, A., Khôi, Đ. N., Dung, T. D., et al. (2020) · Desalination and Water Treatment · doi:10.5004/dwt.2020.26160 · Cited by 30 · see citing works
An Improved Attention-based Bidirectional LSTM Model for Cyanobacterial Bloom Prediction
Ni, J., Liu, R., Tang, G., & Xie, Y. (2022) · International Journal of Control Automation and Systems · doi:10.1007/s12555-021-0802-9 · Cited by 30 · see citing works
Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model
Baek, S., Pyo, J., Kwon, Y. S., Chun, S.-J., Baek, S. H., Ahn, C., Oh, H., Kim, Y. O., & Cho, K. H. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.729954 · Cited by 29 · see citing works
Swift hydraulic models for real-time control applications in sewer networks
Li, J., Sharma, K. R., Li, W., & Yuan, Z. (2022) · Water Research · doi:10.1016/j.watres.2022.118141 · Cited by 29 · see citing works
Kiran, M. G., Das, R., Behera, S. K., Pakshirajan, K., & Das, G. C. (2020) · Water Science & Technology Water Supply · doi:10.2166/ws.2020.304 · Cited by 28 · see citing works
Meng, S., Chen, X., Wang, J., Li, F., Qiu, L. P., Zheng, Y., Chen, J. Z., & Xu, P. (2021) · Frontiers in Environmental Science · doi:10.3389/fenvs.2021.690191 · Cited by 27 · see citing works
Luo, A., Chen, H., Gao, X., Carvalho, L., Xue, Y., Jin, L., & Yang, J. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154172 · Cited by 27 · see citing works
Alharbi, B. S. (2023) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2023.100935 · Cited by 27 · see citing works
Kim, S., Quiroz‐Arita, C., Monroe, E. A., Siccardi, A. J., Mitchell, J., Huysman, N. D., & Davis, R. (2021) · Water Research · doi:10.1016/j.watres.2021.116816 · Cited by 25 · see citing works
Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine
Yi, H.-S., Lee, B.-M., Park, S., Kwak, K.-C., & An, K. (2018) · Environmental Engineering Research · doi:10.4491/eer.2018.245 · Cited by 25 · see citing works
Ebrahimzadeh, G., Alimohammadi, M., Kahkah, M. R. R., & Mahvi, A. H. (2021) · Journal of Environmental Health Science and Engineering · doi:10.1007/s40201-021-00616-x · Cited by 24 · see citing works
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Nieto, P. J. G., García–Gonzalo, E., Lasheras, F. S., Fernández, J. R. A., & Muñiz, C. D. (2019) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2019.112417 · Cited by 23 · see citing works
Najib, N. A. M., Sethu, V., Arumugasamy, S. K., & Selvarajoo, A. (2020) · Environmental Processes · doi:10.1007/s40710-020-00431-w · Cited by 23 · see citing works
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Jain, S., Rathee, S., Kumar, A., Sambasivam, A., Boadh, R., Choudhary, T., Kumar, P., & Singh, P. K. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.01.067 · Cited by 20 · see citing works
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Aranay, O. M., & Atrey, P. K. (2022) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2022.3163229 · Cited by 6 · see citing works
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Townhill, B. L., Tinker, J., Jones, M., Pitois, S., Créach, V., Simpson, S. D., Dye, S. R., Bear, E. A., & Pinnegar, J. K. (2019) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsy203 · Cited by 6 · see citing works
Li, X., Li, M., Zhang, Y., & Deng, X. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9602761 · Cited by 4 · see citing works
Guzel, H. O. (2019) · NDSU Repository (North Dakota State University) · Cited by 4 · see citing works
Ward, S. (2021) · Digital Commons - USU (Utah State University) · doi:10.26076/cc1a-7e4a · Cited by 3 · see citing works
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Baohui, 韩. H., Jiajin, 李. L., Junqiu, 雷. L., Zheng, 胡. H., Hui, 马. M., Ran, 廖. L., & Wenbo, 孙. S. (2026) · Acta Optica Sinica · doi:10.3788/aos260832
Tripathi, G. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70305
Noor, M. H. M., Rabuni, M. F., Hashim, N. A., Ngadi, N., Mohamed, N. B., & Bahrudin, F. I. (2026) · Water · doi:10.3390/w18151862
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Self-citationLiu, H., Liu, F., Zhang, Y., Lopez, C., Yoosefdoost, I., & Li, B. (2026) · Algal Research · doi:10.1016/j.algal.2026.104971
Tiberi, A. E., Drozd, A. A., Zerda, H. R., & Pinto, P. D. T. (2026) · Hydrobiologia · doi:10.1007/s10750-026-06415-5
Shija, G. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2741614
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- Authors
- Sheik, A. G., Kumar, A., Patnaik, R., Kumari, S., & Bux, F.
- Affiliations
- Durban University of Technology (ZA)
- Corresponding author
- Bux, Faizal
- Venue
- Critical Reviews in Environmental Science and Technology
- Publisher
- Informa UK Limited
- Published
- 2023-09-07
- Volume
- 54
- Issue
- 7
- Pages
- 509-532
- ISSN
- 1064-3389 (ISSN-L)
- 1547-6537
- OpenAlex
- W4386496371
- Cited by
- 47
- References
- 128
- Citations by year
- 2026: 20 · 2025: 17 · 2024: 10
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Reviews in Environmental Science and Technology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- algal blooms, machine learning, algal bloom prediction, freshwater ecosystems, artificial neural networks, random forest, support vector machine, long short-term memory, model interpretability, real-time monitoring, nutrient prediction, feature engineering, model benchmarking
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