Implementation of smart buoys and satellite-based systems for the remote monitoring of harmful algae bloom in inland waters
Abstract
The deployment of Remote Monitoring Systems (RMS) based on Internet of Things (IoT) technologies and data management in the cloud is a cutting-edge solution for water management agencies to track harmful algae blooms (HABs). However, each environment presents different intrinsic characteristics, hence the reliability and representativeness of collected data may vary according to the selected RMS architecture. This study aims to implement the same RMS in two different water bodies in Spain: the freshwater As Conchas reservoir and the shallow L'Albufera brackish water lagoon. We firstly evaluate the pros and cons of current technologies available on the market and then we select an RMS based on a combination of plug-and-play (YSI-based) and customisable solutions (Libelium-based). The deployment of nodes on buoys allows big data collection from different areas of water bodies, their continuous transfer and storage on a web server, and their subsequent visualisation on the web interface in real time. Secondly, in order to know whether the selected RMS architecture and monitored environment influence the representativeness of the collected data, we perform a Pearson correlation test between the deployed nodes and satellite images. The results point out that the more heterogeneous the environment is, the more nodes must be deployed in different areas for a longer time to obtain a realistic view of the water body status. Therefore, this study provides critical and empirical data to implement a profitable and effective real-time monitoring system in other HAB-affected areas.
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Literature reviews often address only a selected group of digital twin problems in connection to certain algal cultivation for biomass production areas such as raceway ponds, closed-loop photobioreactor planning, framework management, shipping and water quality.
“Literature reviews often address only a selected group of DT problems in connection to certain ACB areas (raceway ponds, closed-loop photobioreactor (PBR) such as planning [27], framework management [28], shipping [29] and water quality [16].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [29]
Cites
Paerl, H. W., & Huisman, J. (2008) · Science · doi:10.1126/science.1155398 · Cited by 2,854 · see citing works
Paerl, H. W., Scott, J. T., McCarthy, M. J., Newell, S. E., Gardner, W. S., Havens, K. E., Hoffman, D. K., Wilhelm, S. W., & Wurtsbaugh, W. A. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.6b02575 · Cited by 720 · see citing works
Buratti, F. M., Manganelli, M., Vichi, S., Stefanelli, M., Scardala, S., Testai, E., & Funari, E. (2017) · Archives of Toxicology · doi:10.1007/s00204-016-1913-6 · Cited by 665 · see citing works
Blondeau‐Patissier, D., Gower, J. F. R., Dekker, A. G., Phinn, S., & Brando, V. (2014) · Progress In Oceanography · doi:10.1016/j.pocean.2013.12.008 · Cited by 607 · see citing works
Future HAB science: Directions and challenges in a changing climate
Wells, M. L., Karlson, B., Wulff, A., Kudela, R. M., Trick, C. G., Asnaghi, V., Berdalet, E., Cochlan, W. P., Davidson, K., Rijcke, M. D., et al. (2019) · Harmful Algae · doi:10.1016/j.hal.2019.101632 · Cited by 464 · see citing works
Global geographical and historical overview of cyanotoxin distribution and cyanobacterial poisonings
Svirčev, Z. B., Lalić, D., Savić, G. B., Tokodi, N., Drobac, D., Chen, L., Meriluoto, J. A. O., & Codd, G. A. (2019) · Archives of Toxicology · doi:10.1007/s00204-019-02524-4 · Cited by 423 · see citing works
Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey
Guobao, X., Shen, W., & Wang, X. (2014) · Sensors · doi:10.3390/s140916932 · Cited by 413 · see citing works
Chowdhury, M. Z., Shahjalal, M. A., Hasan, M. K., & Jang, Y. M. (2019) · Applied Sciences · doi:10.3390/app9204367 · Cited by 303 · see citing works
Modelling eutrophication in lake ecosystems: A review
Vinçon‐Leite, B., & Casenave, C. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.09.320 · Cited by 265 · see citing works
Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins
Mantzouki, E., Lürling, M., Fastner, J., Domis, L. N. D. S., Wilk‐Woźniak, E., Koreivienė, J., Seelen, L. M. S., Teurlincx, S., Verstijnen, Y. J. M., Krztoń, W., et al. (2018) · Toxins · doi:10.3390/toxins10040156 · Cited by 210 · see citing works
Akçaalan, R., Albay, M., Kakillioğlu, T., & Bahçebaşı, T. (2012) · Cited by 196 · see citing works
Recent Advances in Ion-selective membrane electrodes for in situ environmental water analysis
Crespo, G. A. (2017) · Electrochimica Acta · doi:10.1016/j.electacta.2017.05.159 · Cited by 193 · see citing works
Raven, J. A., Gobler, C. J., & Hansen, P. J. (2020) · Harmful Algae · doi:10.1016/j.hal.2019.03.012 · Cited by 185 · see citing works
A novel single-parameter approach for forecasting algal blooms
Xiao, X., He, J., Huang, H., Miller, T. R., Christakos, G., Reichwaldt, E. S., Ghadouani, A., Lin, S. C., Xu, X., & Shi, J. (2016) · Water Research · doi:10.1016/j.watres.2016.10.076 · Cited by 152 · see citing works
Mantzouki, E., Visser, P., Bormans, M., & Ibelings, B. W. (2015) · Aquatic Ecology · doi:10.1007/s10452-015-9526-3 · Cited by 143 · see citing works
Pereira-Sandoval, M., Ruescas, A. B., Urrego, P., Ruíz-Verdú, A., Delegido, J., Tenjo, C., Sòria‐Perpinyà, X., Vicente, E., Soria, J. M., & Moreno, J. (2019) · Remote Sensing · doi:10.3390/rs11121469 · Cited by 138 · see citing works
Chang, N., Imen, S., & Vannah, B. W. (2014) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2013.829981 · Cited by 136 · see citing works
Algal bloom and its economic impact
Sanseverino, I., Sofia, C. A. D., Pozzoli, L., Dobricic, S., & Lettieri, T. (2016) · Joint Research Centre (European Commission) · doi:10.2788/660478 · Cited by 133 · see citing works
Stauffer, B., Bowers, H. A., Buckley, E. N., Davis, T. W., Johengen, T. H., Kudela, R. M., McManus, M. A., Purcell, H., Smith, G. J., Woude, A. V., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00399 · Cited by 129 · see citing works
Romo, S., Villena, M., Sahuquillo, M., Soria, J. M., GIMÉNEZ, M., Alfonso, T., Vicente, E., & Miracle, M. R. (2005) · Freshwater Biology · doi:10.1111/j.1365-2427.2005.01432.x · Cited by 122 · see citing works
How to model algal blooms in any lake on earth
Janssen, A. B. G., Janse, J. H., Beusen, A. H. W., Chang, M., Harrison, J. A., Huttunen, I., Kong, X., Rost, J., Teurlincx, S., Troost, T. A., et al. (2018) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2018.09.001 · Cited by 102 · see citing works
Costs of harmful blooms of freshwater cyanobacteria
Hamilton, D. P., Wood, S. A., Dietrich, D. R., & Puddick, J. (2013) · doi:10.1002/9781118402238.ch15 · Cited by 86 · see citing works
Hamilton, D. P., Carey, C. C., Арвола, Л., ARZBERGER, P. W., Brewer, C., Cole, J. J., Gaiser, E. E., Hanson, P. C., Ibelings, B. W., Jennings, E., et al. (2015) · Inland Waters · doi:10.5268/iw-5.1.566 · Cited by 85 · see citing works
Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?
Bertani, I., Steger, C., Obenour, D. R., Fahnenstiel, G. L., Bridgeman, T. B., Johengen, T. H., Sayers, M. J., Shuchman, R. A., & Scavia, D. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.10.023 · Cited by 78 · see citing works
Past, present and future of la Albufera of Valencia Natural Park
Soria, J. M. (2006) · Limnetica · doi:10.23818/limn.25.10 · Cited by 75 · see citing works
Schmidt, W., Raymond, D., Parish, D., Ashton, I. G. C., Miller, P. I., Campos, C., & Shutler, J. D. (2018) · Aquacultural Engineering · doi:10.1016/j.aquaeng.2017.12.002 · Cited by 69 · see citing works
Barros, M. U. G., Wilson, A. E., Leitão, J. I. R., Pereira, S. P., Buley, R. P., Fernández-Figueroa, E. G., & Neto, J. C. (2019) · Harmful Algae · doi:10.1016/j.hal.2019.05.006 · Cited by 69 · see citing works
Mantzouki, E., Campbell, J., van Loon, E. E., Visser, P., Konstantinou, I., Antoniou, M., Giuliani, G., Machado-Vieira, D., de Oliveira, A. G., Maronić, D. Š., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.226 · Cited by 61 · see citing works
Mishra, D. R., Kumar, A., Ramaswamy, L. M., Boddula, V., Das, M. C., Page, B. P., & Weber, S. J. (2020) · Harmful Algae · doi:10.1016/j.hal.2020.101828 · Cited by 60 · see citing works
Duffy, G., & Regan, F. B. (2017) · The Analyst · doi:10.1039/c7an00840f · Cited by 48 · see citing works
Chaffin, J. D., Kane, D. D., Stanislawczyk, K., & Parker, E. M. (2018) · Environmental Science and Pollution Research · doi:10.1007/s11356-018-2612-z · Cited by 46 · see citing works
Cyanobacteria in Spanish reservoirs. How frequently are they toxic?
Quesada, A. (2004) · Limnetica · doi:10.23818/limn.23.09 · Cited by 42 · see citing works
Pereira-Sandoval, M., Urrego, P., Ruíz-Verdú, A., Tenjo, C., Delegido, J., Sòria‐Perpinyà, X., Vicente, E., & Soria, J. M. (2019) · Limnetica · doi:10.23818/limn.38.27 · Cited by 40 · see citing works
Yang, J., Holbach, A., Wilhelms, A., Qin, Y., Zheng, B., Zou, H., Qin, B., Zhu, G., & Norra, S. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.044 · Cited by 38 · see citing works
Wang, J., Jiang, X., Zheng, B., Chen, C., Kang, X., Zhang, C., Song, Z., Wang, K., Wang, W., & Wang, S. (2015) · Environmental Earth Sciences · doi:10.1007/s12665-015-4810-z · Cited by 35 · see citing works
Choo, F., Zamyadi, A., Stuetz, R. M., Newcombe, G., Newton, K., & Henderson, R. K. (2018) · Water Research · doi:10.1016/j.watres.2018.10.034 · Cited by 35 · see citing works
Wireless Communication Technologies in Internet of Things: A Critical Evaluation
Karunarathne, G. G. K. W. M. S. I. R., Kulawansa, K., & Firdhous, M. (2018) · International Conference on Intelligent and Innovative Computing Applications · doi:10.1109/iconic.2018.8601226 · Cited by 35 · see citing works
A Review of Turbidity Detection Based on Computer Vision
Liu, Y., Chen, Y., & Fang, X. (2018) · IEEE Access · doi:10.1109/access.2018.2875071 · Cited by 33 · see citing works
Remotely sensing harmful algal blooms in the Red Sea
Gokul, E. A., Raitsos, D. Ε., Gittings, J. A., Alkawri, A., & Hoteit, I. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215463 · Cited by 32 · see citing works
Tao, M., Duan, H., Cao, Z., Loiselle, S., & Ma, R. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2723079 · Cited by 30 · see citing works
Rodrigues, G., Potes, M., Costa, M. J., Novais, M. H., Penha, A. M., Salgado, R., & Morais, M. (2020) · Remote Sensing · doi:10.3390/rs12050768 · Cited by 25 · see citing works
Monitoring Cyanobacteria Blooms in Freshwater Lakes using Remote Sensing Methods
Kubiak, K., Kotlarz, J., Czapski, P. P., & Mazur, A. (2015) · Polish Journal of Environmental Studies · doi:10.15244/pjoes/60175 · Cited by 22 · see citing works
Chaffin, J. D., Kane, D. D., & Johnson, A. (2020) · Journal of Environmental Sciences · doi:10.1016/j.jes.2020.03.003 · Cited by 18 · see citing works
Myers, D. N. (2019) · Separation science and technology · doi:10.1016/b978-0-12-815730-5.00010-7 · Cited by 17 · see citing works
Data-Driven Soft Sensor Modeling for Algal Blooms Monitoring
Wang, Z., Zhao, Z., Li, D., & Cui, L. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2350497 · Cited by 13 · see citing works
Honti, M., Istvánovics, V., Stæhr, P. A. U., Brighenti, L. S., Zhu, M., & Zhu, G. (2016) · Inland Waters · doi:10.1080/iw-6.4.877 · Cited by 12 · see citing works
Honti, M., Istvánovics, V., Stæhr, P. A. U., et al. (2016) · Scopus (Elsevier) · doi:10.5268/iw-6.4.877 · Cited by 11 · see citing works
Oyama, Y., Matsushita, B., & Fukushima, T. (2016) · Ecological research monographs · doi:10.1007/978-981-10-0780-4_6 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bhardwaj, A., Dagar, V., Khan, M. O., Aggarwal, A., Alvarado, R., Kumar, M., Irfan, M. F., & Proshad, R. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-19014-3 · Cited by 119 · see citing works
Smart Water Quality Monitoring with IoT Wireless Sensor Networks
Singh, Y., & Walingo, T. M. (2024) · Sensors · doi:10.3390/s24092871 · Cited by 137 · see citing works
Rahu, M. A., Chandio, A. F., Aurangzeb, K., Karim, S., Alhussein, M. A., & Anwar, M. S. (2023) · IEEE Access · doi:10.1109/access.2023.3315649 · Cited by 89 · 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
Rahman, G. M. E., & Wahid, K. A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079096 · Cited by 38 · see citing works
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
Mapping Harmful Algae Blooms: The Potential of Hyperspectral Imaging Technologies
Arias, F. X., Zambrano, M., Galagarza, E. S., & Broce, K. (2025) · Remote Sensing · doi:10.3390/rs17040608 · Cited by 34 · see citing works
Hagh, S. F., Amngostar, P., Zylka, A., Zimmerman, M., Cresanti, L., Karins, S., O’Neil-Dunne, J. P., Ritz, K., Williams, C. J., Morales‐Williams, A. M., et al. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3364142 · Cited by 30 · see citing works
Deep learning methods for multi-horizon long-term forecasting of Harmful Algal Blooms
Martín-Suazo, S., Morón-López, J., Vakaruk, S., Karamchandani, A., Pascual-Aguilar, J. A., Mozó, A., Gómez-Canaval, S., Vinyals, M., & Ortiz, J. M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112279 · Cited by 25 · see citing works
Self-citationFetching Ecosystem Monitoring Data in Extreme Areas via a Drone-Enabled Internet of Remote Things
Zhang, M., Zhang, L., Zhao, C., Jin, R., Guo, J., & Li, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3195302 · Cited by 24 · see citing works
Multiple Channel LoRa-to-LEO Scheduling for Direct-to-Satellite IoT
Tondo, F. A., Afhamisis, M., Montejo‐Sánchez, S., López, O. L. A., Palattella, M. R., & Souza, R. D. (2024) · IEEE Access · doi:10.1109/access.2024.3368872 · Cited by 23 · see citing works
Advanced Water Quality Assessment with IOT WSN and Multivariate Machine Learning Techniques
Shofia Priyadharshini, D., & Ramesh, G. P. (2025) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs65178.2025.10968136 · Cited by 21 · see citing works
Cache Control of Edge Computing System for Tradeoff Between Delays and Cache Storage Costs
Hou, C., Zhou, C., Huang, Q., & Yan, C.-B. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3228250 · Cited by 18 · see citing works
Optimal Traffic Load Allocation for Aloha-Based IoT LEO Constellations
Tondo, F. A., Souto, V., López, O. A., Montejo-Sánchez, S., Céspedes, S., & Souza, R. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3230796 · Cited by 17 · see citing works
Pazo, M., Gerassis, S., Araújo, M., Antunes, I. M. H. R., & Rigueira, X. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.172340 · Cited by 16 · see citing works
Amngostar, P., Dehabadi, M., Hagh, S. F., Badireddy, A. R., Huston, D. R., & Xia, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3495712 · Cited by 12 · see citing works
Arroyo, P., Herrero, J. L., Lozano, J., & Montero, P. (2022) · Electronics · doi:10.3390/electronics11121865 · Cited by 12 · see citing works
Navigating aquaculture losses: Tackling fish‐killing phytoflagellates in a changing global landscape
Allaf, M. M., & Erratt, K. J. (2024) · Reviews in Aquaculture · doi:10.1111/raq.12947 · Cited by 9 · see citing works
A holistic water monitoring approach for an effective ecosystem management
Morón-López, J. (2021) · Ecohydrology & Hydrobiology · doi:10.1016/j.ecohyd.2021.08.003 · Cited by 9 · see citing works
Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review
Karin, I., Medvešek, I. G., & Šoda, J. (2025) · Applied Sciences · doi:10.3390/app15073452 · Cited by 9 · see citing works
Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
Rojo-García, J. I., Vera-Chavarría, S. A., Lozano-Hernández, Y., Meza, V. G. S., González‐Sierra, J., & Oliva-Moreno, L. N. (2024) · Future Internet · doi:10.3390/fi16060186 · Cited by 7 · see citing works
Jechow, A., Bumberger, J., Palm, B., Remmler, P., Schreck, G., Ogashawara, I., Kiel, C., Kohnert, K., Grossart, H., Singer, G. A., et al. (2024) · Sensors · doi:10.3390/s24196445 · Cited by 6 · see citing works
Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms
Khan, F. A., Gincley, B., Busch, A. E., Tolofari, D. L., Norton, J., Varga, E., McKay, R. M. L., Fuentes‐Cabrera, M., Slawecki, T., & Pinto, A. J. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c06078 · Cited by 6 · see citing works
Sun, L., Zhang, Z., Li, Y., Zhang, L., Chen, Q., Yu, R., Hao, Y., & Lü, C. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23781-4 · Cited by 6 · see citing works
Gao, S., Ren, X., Zhang, Y., & Tang, H. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.06.016 · Cited by 5 · see citing works
Fort, A., Mugnaini, M., Peruzzi, G., & Pozzebon, A. (2022) · IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) · doi:10.1109/metrosea55331.2022.9950856 · Cited by 5 · see citing works
Unlocking the algae toolbox: Cutting-edge tools for environmental and biotechnological solutions
Bui, V. K. H., Trung, L. G., Huu, H., Tran, N. T., & Nguyen, M. K. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108652 · Cited by 4 · see citing works
Rady, M., Muñoz, J., Abu-Aisheh, R., Vučinić, M., Tobar, J. A., Cortes, A., Lampin, Q., Barthel, D., & Watteyne, T. (2022) · Sensors · doi:10.3390/s22197586 · Cited by 4 · see citing works
USV-Buoy Assisted Multiuser MIMO Transmission in Cache-Enabled Maritime Wireless Networks
Zeng, C., Wang, J.-B., Pan, Y., Chen, Y., Yu, H., Liu, W., Wang, Y., Xiao, M., & Wang, J. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3528915 · Cited by 3 · see citing works
Zeng, C., Wang, J.-B., Pan, Y., Xiao, M., Chang, C., Zhang, X., Chen, Y., Yu, H., & Wang, J. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3551632 · Cited by 3 · see citing works
Development of a Fuzzy Inference Based Solar Energy Controller for Smart Marine Water Monitoring
Kalibatienė, D., Miliauskaitė, J., Dzemydienė, D., & Maskeliūnas, S. (2021) · Informatica · doi:10.15388/21-infor470 · Cited by 2 · see citing works
Research on a Mobile Hydrological Detection System Based on BDS
Ning, J., Wang, R., & Zhou, J. (2023) · International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA) · doi:10.1145/3641343.3641351 · Cited by 1 · see citing works
Bhardwaj, A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815256710124010004 · Cited by 1 · see citing works
Lanuza, R. V., Magday, R. D. F., & Yumang, A. N. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730345 · Cited by 1 · see citing works
Forecasting Cyanobacteria Cell Counts in Lakes Based on Hyperspectral Sensing
Nguyen, D., Malthus, T., Anstee, J. M., Biswas, T., Kenna, E., Carbery, M., & Jöhnk, K. (2025) · Remote Sensing · doi:10.3390/rs17193269 · Cited by 1 · see citing works
Bu, J., Liu, S., Yang, H., Zhao, Y., Wang, Q., He, X., Zuo, X., & Wu, H. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3673308 · Cited by 1 · see citing works
A Conceptual Software Framework to Monitor Harmful Algal Blooms (HABs) in Lakes
Sujatha, M. G., Reza, H., & Ranganathan, P. (2024) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc60891.2024.10427825 · Cited by 1 · see citing works
Correlation Between Big Data and Cloud in the Transactions Environment
Patrcia, A. C. M., Bernard, S. K., & Lagasane, K. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290308 · Cited by 1 · see citing works
Tsoloane, M., Tshabalala, A., Sidumo, A., & Singo, M. (2025) · Research Square · doi:10.21203/rs.3.rs-7329922/v1
Jamero, G. C., & Tacda, E. A. (2025) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap68205.2025.11222256
Satellite-Connected Buoy for Environmental Monitoring in Remote Andean Reservoirs
Catari-Alanoca, J. G., Santa‐Maria, M. C., Lobo, F. D. L., Gonzales, R., Ardiles, R. D. N., & Saravia, D. C. A. (2025) · IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) · doi:10.1109/intercon67304.2025.11244667
Singo, S., Sidumo, A., Tsoloane, M., & Tshabalala, A. J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6110227
Large Language Models for Causal Inference in Eutrophication Control of the as Conchas Reservoir
Soilán, M., Araújo, M., Balado, J., & Pazo, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-16995-2_8
Climate-mediated food safety hazards of aquaculture
Fairfield, W. D., Nair, N. K., Klinger, D. H., & Golden, C. D. W. (2026) · Environmental Research Food Systems · doi:10.1088/2976-601x/ae854f
Transformer-based soft-sensors for high-frequency chlorophyll-a estimation in algal bloom monitoring
Martín-Suazo, S., Morón-López, J., Mozó, A., & Gómez-Canaval, S. (2026) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2026.107192
- Authors
- Moron-Lopez, J., Rodriguez-Sanchez, M. C., Carreno, F., Vaquero, J., Pompa-Pernia, A. G., Mateos-Fernandez, M., & Pascual Aguilar, J. A.
- Venue
- IEEE Sensors Journal
- Published
- 2021-03-01
- Volume
- 21
- Issue
- 5
- Pages
- 6990-6997
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3109271829
- Cited by
- 46
- References
- 47
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 14 · 2023: 3 · 2022: 10 · 2021: 3
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- BURJC Digital (King Juan Carlos University) (submittedVersion)
- Hispana (submittedVersion)
- Funders
- Fundación Biodiversidad, the Ministry for Ecological Transition and the Demographic Challenge (CA_CC_2018)
- MINECO (RTC-2016-5087-2)
- programme of the Community of Madrid (PROMINT-CM S2018/EMT-4366)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Keywords
- harmful algal blooms, remote monitoring system, smart buoy, Internet of Things, satellite monitoring, inland waters, brackish lagoon, real-time water quality monitoring, data representativeness, sensor deployment, cloud data management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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