Aplicaciones de inteligencia artificial para la gestión sostenible del agua: una revisión sistemática de la literatura
Palabras clave:
Gestión sostenible del agua, Inteligencia artificial, Cambio climático, Pronóstico del aguaResumen
Introducción. La gestión eficiente de los recursos hídricos es crucial ante el cambio climático y la creciente demanda de agua. La inteligencia artificial se perfila como una herramienta clave para optimizar y hacer más sostenible su gestión. Este artículo examina la aplicación de la IA en la gestión del agua, destacando sus beneficios, desafíos e implementación. Trabajo relacionado: Estudios previos han explorado la IA en el modelado predictivo, los sistemas de monitoreo y la toma de decisiones. Sin embargo, su adopción práctica sigue siendo limitada debido a barreras técnicas y socioeconómicas. Metodología: Este estudio realizó una revisión bibliográfica sobre la aplicación de la inteligencia artificial en la gestión del agua, analizando investigaciones teóricas y estudios de caso, y centrándose en tecnologías y contextos de aplicación. Resultados: Los resultados muestran que la IA puede optimizar el uso del agua y mejorar la respuesta ante emergencias, pero enfrenta limitaciones en la disponibilidad de datos y el acceso a la tecnología. Análisis de resultados: A pesar de su potencial, la implementación de la inteligencia artificial enfrenta desafíos, como la calidad de los datos y la accesibilidad a la tecnología. Conclusiones: La inteligencia artificial tiene un gran potencial, pero es necesario superar desafíos como la calidad de los datos y un mejor acceso a la tecnología para maximizar sus beneficios.
Descargas
Referencias
C. Estrela-Segrelles, M. Á. Pérez-Martín, and Q. J. Wang, ‘Adapting Water Resources Management to Climate Change in Water-Stressed River Basins—Júcar River Basin Case’, Water, vol. 16, no. 7, Art. no. 7, Jan. 2024, doi: 10.3390/w16071004.
M. Ciampittiello, A. Marchetto, and A. Boggero, ‘Water Resources Management under Climate Change: A Review’, Sustainability, vol. 16, no. 9, Art. no. 9, Jan. 2024, doi: 10.3390/su16093590.
K. Furtak and A. Wolińska, ‘The impact of extreme weather events as a consequence of climate change on the soil moisture and on the quality of the soil environment and agriculture – A review’, CATENA, vol. 231, p. 107378, Oct. 2023, doi: 10.1016/j.catena.2023.107378.
Intergovernmental Panel on Climate Change (IPCC), Ed., ‘Weather and Climate Extreme Events in a Changing Climate’, in Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge: Cambridge University Press, 2023, pp. 1513–1766. doi: 10.1017/9781009157896.013.
G. Howard, R. Calow, A. Macdonald, and J. Bartram, ‘Climate Change and Water and Sanitation: Likely Impacts and Emerging Trends for Action’, Annual Review of Environment and Resources, vol. 41, no. Volume 41, 2016, pp. 253–276, Oct. 2016, doi: 10.1146/annurev-environ-110615-085856.
G. M. MacDonald, ‘Water, climate change, and sustainability in the southwest’, Proceedings of the National Academy of Sciences, vol. 107, no. 50, pp. 21256–21262, Dec. 2010, doi: 10.1073/pnas.0909651107.
C. Ringler et al., ‘The role of water in transforming food systems’, Global Food Security, vol. 33, p. 100639, Jun. 2022, doi: 10.1016/j.gfs.2022.100639.
‘The United Nations world water development report 2018: nature-based solutions for water - UNESCO Biblioteca Digital’. Accessed: Apr. 14, 2025. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000261424
J. F. Velasco-Muñoz, J. A. Aznar-Sánchez, L. J. Belmonte-Ureña, and I. M. Román-Sánchez, ‘Sustainable Water Use in Agriculture: A Review of Worldwide Research’, Sustainability, vol. 10, no. 4, Art. no. 4, Apr. 2018, doi: 10.3390/su10041084.
UNESCO, UN-Water, and W. W. A. Programme, The United Nations World Water Development Report 2020 :: water and climate change. UNESCO, 2020. Accessed: Apr. 14, 2025. [Online]. Available: https://digitallibrary.un.org/record/3892703
B. Grizzetti, A. Pistocchi, C. Liquete, A. Udias, F. Bouraoui, and W. van de Bund, ‘Human pressures and ecological status of European rivers’, Sci Rep, vol. 7, no. 1, p. 205, Mar. 2017, doi: 10.1038/s41598-017-00324-3.
S. SAVALKAR and N. PATIL, ‘Artificial Intelligence in Water Resource Management: The Past, Present and Opportunities thereof’, ResearchGate. Accessed: Apr. 14, 2025. [Online]. Available: https://www.researchgate.net/publication/368348809_Artificial_Intelligence_in_Water_Resource_Management_The_Past_Present_and_Opportunities_thereof
P. Bridgewater, A. Loyau, and D. S. Schmeller, ‘The seventh plenary of the intergovernmental platform for biodiversity and ecosystem services (IPBES-7): a global assessment and a reshaping of IPBES’, Biodivers Conserv, vol. 28, no. 10, pp. 2457–2461, Aug. 2019, doi: 10.1007/s10531-019-01804-w.
Q. Xu, Y. Shi, J. Bamber, Y. Tuo, R. Ludwig, and X. X. Zhu, ‘Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology’, Jul. 12, 2024, arXiv: arXiv:2310.05227. doi: 10.48550/arXiv.2310.05227.
A. E. Din Mahmoud, M. Fawzy, and N. Ahmad Khan, Artificial Intelligence and Modeling for Water Sustainability: Global Challenges. 2023. Accessed: Apr. 20, 2025. [Online]. Available: https://www.routledge.com/Artificial-Intelligence-and-Modeling-for-Water-Sustainability-Global-Challenges/Mahmoud-Fawzy-Khan/p/book/9781032197074
Y. N. Deshvena and S. M. Deshpande, Artificial Intelligence For Real-time Water Management, 02 ed., vol. 11. 2024. Accessed: Apr. 20, 2025. [Online]. Available: https://journals.stmjournals.com/jowrem/article=2024/view=170546/
R. Baena-Navarro, Y. Carriazo-Regino, F. Torres-Hoyos, and J. Pinedo-López, ‘Intelligent Prediction and Continuous Monitoring of Water Quality in Aquaculture: Integration of Machine Learning and Internet of Things for Sustainable Management’, Water, vol. 17, no. 1, Art. no. 1, Jan. 2025, doi: 10.3390/w17010082.
Y. Tfifha, M. Ennahedh, and N. Debbabi, ‘Artificial Intelligence-Based Decision Support System for Groundwater Management Under Climate Change: Application to Mornag Plain in Tunisia’, in Recent Advancements from Aquifers to Skies in Hydrogeology, Geoecology, and Atmospheric Sciences, H. Chenchouni, Z. Zhang, D. S. Bisht, M. Gentilucci, M. Chen, H. I. Chaminé, M. Barbieri, M. K. Jat, J. Rodrigo-Comino, D. Panagoulia, A. Kallel, A. Biswas, V. Turan, J. Knight, A. Çiner, C. Candeias, and Z. A. Ergüler, Eds., Cham: Springer Nature Switzerland, 2024, pp. 15–20. doi: 10.1007/978-3-031-47079-0_4.
Samirsinh P Parmar, ‘Water Resource Management Using Artificial Intelligence Enabled RS & GIS’, Apr. 2023, doi: 10.5281/ZENODO.7878771.
U. Otamendi, M. Maiza, I. G. Olaizola, B. Sierra, M. Florez, and M. Quartulli, ‘Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach’, Journal of Environmental Management, vol. 370, p. 122526, Nov. 2024, doi: 10.1016/j.jenvman.2024.122526.
T. Takeda, J. Kato, T. Matsumura, T. Murakami, and A. Abeynayaka, ‘Governance of Artificial Intelligence in Water and Wastewater Management: The Case Study of Japan’, Hydrology, vol. 8, no. 3, Art. no. 3, Sep. 2021, doi: 10.3390/hydrology8030120.
M. J. Page et al., ‘The PRISMA 2020 statement: an updated guideline for reporting systematic reviews’, BMJ, vol. 372, p. n71, Mar. 2021, doi: 10.1136/bmj.n71.
M. L. Rethlefsen et al., ‘PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews’, Systematic Reviews, vol. 10, no. 1, p. 39, Jan. 2021, doi: 10.1186/s13643-020-01542-z.
D. E. Green, ‘¿Qué es la gestión sostenible del agua?’, Sigma Earth. Accessed: Apr. 25, 2025. [Online]. Available: https://sigmaearth.com/es/%C2%BFQu%C3%A9-es-la-gesti%C3%B3n-sostenible-del-agua%3F/
SERVIR, ‘Gestión sostenible del agua’. 2021. [Online]. Available: https://cdn.www.gob.pe/uploads/document/file/2679238/Gesti%C3%B3n%20sostenible%20del%20agua.pdf
Red del Agua UNAM, ‘Convergencia tecnológica para la gestión sustentable del agua’. 2023.
V. García Benítez and E. A. Ruvalcaba-Gómez, ‘Análisis de las estrategias nacionales de inteligencia artificial en América Latina: Estudio de los enfoques de ética y de derechos humanos’, Revista de Gestión Pública, vol. 10, no. 1, pp. 5–32, 2021, Accessed: Apr. 25, 2025. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=8431842
CEPAL, ‘Tecnologías digitales para un nuevo futuro’, 2021.
V. Baños-Gonzalez, ‘La inteligencia artificial, estudio de su evolución y aplicación en México’, Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, vol. 12, pp. 250–260, Nov. 2024, doi: 10.29057/icbi.v12iEspecial4.13338.
A. Mosavi, P. Ozturk, and K. Chau, ‘Flood Prediction Using Machine Learning Models: Literature Review’, Water, vol. 10, no. 11, Art. no. 11, Nov. 2018, doi: 10.3390/w10111536.
S. Gharbia et al., ‘Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale’, Sustainability, vol. 14, no. 7, Art. no. 7, Jan. 2022, doi: 10.3390/su14074037.
A. Rogel Rojas, A. Hidalgo Velastegui, F. Castro Solórzano, F. Morales Fiallos, D. Moya Medina, and B. Paredes-Beltran, ‘Aplicación de Redes Neuronales Artificiales para la Estimación de Pre-cipitaciones: Caso de Estudio de la Cuenca del Río Pastaza, Ecuador’, Revista Científica y Arbitrada del Observatorio Territorial, Artes y Arquitectura: FINIBUS, vol. 7, no. 14, pp. 131–146, Jul. 2024, doi: 10.56124/finibus.v7i14.013.
W. F. L. Vilca, ‘Aplicación de Redes Neuronales Artificiales a la Modelización y Previsión de Caudales Medios Mensuales del Río Huancané’, 2010.
A. F. Ruiz Hurtado, ‘Estimación de la precipitación en la cuenca hidrográfica del río Bolo con técnicas de inteligencia artificial’, Trabajo de grado - Maestría, Universidad Nacional de Colombia, 2023. Accessed: Apr. 25, 2025. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/87439
R. Zamani, A. M. A. Ali, and A. Roozbahani, ‘Evaluation of Adaptation Scenarios for Climate Change Impacts on Agricultural Water Allocation Using Fuzzy MCDM Methods’, Water Resour Manage, vol. 34, no. 3, pp. 1093–1110, Feb. 2020, doi: 10.1007/s11269-020-02486-8.
M. R. A. Shehhi and A. Kaya, ‘Time series and machine learning to forecast the water quality from satellite data’, Mar. 16, 2020, arXiv: arXiv:2003.11923. doi: 10.48550/arXiv.2003.11923.
W. N. S. Zondo, J. T. Ndoro, and V. Mlambo, ‘The Adoption and Impact of Climate-Smart Water Management Technologies in Smallholder Farming Systems of Sub-Saharan Africa: A Systematic Literature Review’, Water, vol. 16, no. 19, Art. no. 19, Jan. 2024, doi: 10.3390/w16192787.
J. T R, N. S. Reddy, and U. D. Acharya, ‘Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches’, Water Resour Manage, vol. 37, no. 3, pp. 1013–1032, Feb. 2023, doi: 10.1007/s11269-022-03399-4.
M. M. Mekonnen and A. Y. Hoekstra, ‘Four billion people facing severe water scarcity’, Science Advances, vol. 2, no. 2, p. e1500323, Feb. 2016, doi: 10.1126/sciadv.1500323.
D. Li and Q. Fu, ‘Deep Learning Model-Based Demand Forecasting for Secondary Water Supply in Residential Communities: A Case Study of Shanghai City, China’, IEEE Access, vol. 12, pp. 38745–38757, 2024, doi: 10.1109/ACCESS.2023.3288817.
E. M. Raouhi, M. Zouizza, M. Lachgar, Y. Zouani, H. Hrimech, and A. Kartit, ‘AIDSII: An AI-based digital system for intelligent irrigation’, Software Impacts, vol. 17, p. 100574, Sep. 2023, doi: 10.1016/j.simpa.2023.100574.
S. Dhal, J. Alvarado, U. Braga-Neto, and B. Wherley, ‘Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation’, Smart Agricultural Technology, vol. 9, p. 100569, Dec. 2024, doi: 10.1016/j.atech.2024.100569.
F. Mortazavizadeh et al., ‘Advances in machine learning for agricultural water management: a review of techniques and applications’, Journal of Hydroinformatics, vol. 27, no. 3, pp. 474–492, Mar. 2025, doi: 10.2166/hydro.2025.258.
S. A. Vergina, D. S. Kayalvizhi, D. R. M. Bhavadharini, and K. Devi, ‘A Real Time Water Quality Monitoring Using Machine Learning Algorithm’, Clinical Medicine, vol. 07, no. 08, 2020.
M. Y. Shams, A. M. Elshewey, E.-S. M. El-kenawy, A. Ibrahim, F. M. Talaat, and Z. Tarek, ‘Water quality prediction using machine learning models based on grid search method’, Multimed Tools Appl, vol. 83, no. 12, pp. 35307–35334, Apr. 2024, doi: 10.1007/s11042-023-16737-4.
Z. Li et al., ‘Applications of machine learning in drinking water quality management: A critical review on water distribution system’, Journal of Cleaner Production, vol. 481, p. 144171, Nov. 2024, doi: 10.1016/j.jclepro.2024.144171.
R. M. Frincu, ‘Artificial intelligence in water quality monitoring: A review of water quality assessment applications’, Water Quality Research Journal, vol. 60, no. 1, pp. 164–176, Nov. 2024, doi: 10.2166/wqrj.2024.049.
N. Rane, S. Choudhary, and J. Rane, ‘Artificial intelligence for enhancing resilience’, Journal of Applied Artificial Intelligence, vol. 5, no. 2, Art. no. 2, Sep. 2024, doi: 10.48185/jaai.v5i2.1053.
C. Wardropper and A. Brookfield, ‘Decision-support systems for water management’, Journal of Hydrology, vol. 610, p. 127928, Jul. 2022, doi: 10.1016/j.jhydrol.2022.127928.
Ó. R. Dolling and E. A. V. Castellón, ‘Sistema de apoyo a la operación de sistemas hídricos con propósitos múltiples, SA SARH-2000’, Tecnología y ciencias del agua, vol. 18, no. 1, Art. no. 1, 2003, Accessed: Apr. 21, 2025. [Online]. Available: https://revistatyca.org.mx/index.php/tyca/article/view/970
A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, ‘Internet of Things for Smart Cities’, IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, Feb. 2014, doi: 10.1109/JIOT.2014.2306328.
Y. Singh and T. Walingo, ‘Smart Water Quality Monitoring with IoT Wireless Sensor Networks’, Sensors, vol. 24, no. 9, Art. no. 9, Jan. 2024, doi: 10.3390/s24092871.
L. García, L. Parra, J. M. Jimenez, J. Lloret, and P. Lorenz, ‘IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture’, Sensors, vol. 20, no. 4, Art. no. 4, Jan. 2020, doi: 10.3390/s20041042.
A. Morchid, R. Jebabra, H. M. Khalid, R. El Alami, H. Qjidaa, and M. Ouazzani Jamil, ‘IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing’, Results in Engineering, vol. 23, p. 102829, Sep. 2024, doi: 10.1016/j.rineng.2024.102829.
K. D., ‘A study on artificial intelligence for monitoring smart environments’, Materials Today: Proceedings, vol. 80, pp. 2009–2013, Jan. 2023, doi: 10.1016/j.matpr.2021.06.046.
T. Miller et al., ‘Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data’, Electronics, vol. 14, no. 4, Art. no. 4, Jan. 2025, doi: 10.3390/electronics14040696.
X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, ‘Data mining with big data’, 2014, Accessed: Apr. 21, 2025. [Online]. Available: https://www.computer.org/csdl/journal/tk/2014/01/ttk2014010097/13rRUxjQych
W. Ouyang, ‘Data Visualization in Big Data Analysis: Applications and Future Trends’, Journal of Computer and Communications, vol. 12, no. 11, Art. no. 11, Oct. 2024, doi: 10.4236/jcc.2024.1211005.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, ‘Google Earth Engine: Planetary-scale geospatial analysis for everyone’, Remote Sensing of Environment, vol. 202, pp. 18–27, Dec. 2017, doi: 10.1016/j.rse.2017.06.031.
H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco, ‘Google Earth Engine for geo-big data applications: A meta-analysis and systematic review’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 152–170, Jun. 2020, doi: 10.1016/j.isprsjprs.2020.04.001.
D. G. Tarboton et al., ‘HydroShare retrospective: Science and technology advances of a comprehensive data and model publication environment for the water science domain’, Environmental Modelling & Software, vol. 172, p. 105902, Jan. 2024, doi: 10.1016/j.envsoft.2023.105902.
N. Kumar Koditala and P. Shekar Pandey, ‘Water Quality Monitoring System Using IoT and Machine Learning’, in 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), Aug. 2018, pp. 1–5. doi: 10.1109/RICE.2018.8509050.
A. U. Egbemhenghe et al., ‘Revolutionizing water treatment, conservation, and management: Harnessing the power of AI-driven ChatGPT solutions’, Environmental Challenges, vol. 13, p. 100782, Dec. 2023, doi: 10.1016/j.envc.2023.100782.
S. R. O. Marshall, Tran ,Thanh-Nhan-Duc, Tapas ,Mahesh R., and B. Q. and Nguyen, ‘Integrating artificial intelligence and machine learning in hydrological modeling for sustainable resource management’, International Journal of River Basin Management, vol. 0, no. 0, pp. 1–17, 2025, doi: 10.1080/15715124.2025.2478280.
H. Mosaffa, M. Sadeghi, I. Mallakpour, M. Naghdyzadegan Jahromi, and H. R. Pourghasemi, ‘Chapter 43 - Application of machine learning algorithms in hydrology’, in Computers in Earth and Environmental Sciences, H. R. Pourghasemi, Ed., Elsevier, 2022, pp. 585–591. doi: 10.1016/B978-0-323-89861-4.00027-0.
C. W. Dawson, ‘Neural Network Solutions to Flood Estimation at Ungauged Sites’, in Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications, R. J. Abrahart, L. M. See, and D. P. Solomatine, Eds., Berlin, Heidelberg: Springer, 2008, pp. 49–57. doi: 10.1007/978-3-540-79881-1_4.
M. S. Oyounalsoud, A. G. Yilmaz, M. Abdallah, and A. Abdeljaber, ‘Drought prediction using artificial intelligence models based on climate data and soil moisture’, Sci Rep, vol. 14, no. 1, p. 19700, Aug. 2024, doi: 10.1038/s41598-024-70406-6.
C. Lalika, A. U. H. Mujahid, M. James, and M. C. S. Lalika, ‘Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania’, Journal of Hydrology: Regional Studies, vol. 53, p. 101794, Jun. 2024, doi: 10.1016/j.ejrh.2024.101794.
W. Weber de Melo, I. Iglesias, and J. Pinho, ‘Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models’, Nat Hazards, vol. 121, no. 4, pp. 4615–4638, Mar. 2025, doi: 10.1007/s11069-024-06957-8.
A. Khanna and S. Kaur, ‘Internet of Things (IoT), Applications and Challenges: A Comprehensive Review’, Wireless Pers Commun, vol. 114, no. 2, pp. 1687–1762, Sep. 2020, doi: 10.1007/s11277-020-07446-4.
M. Pule, A. Yahya, and J. Chuma, ‘Wireless sensor networks: A survey on monitoring water quality’, Journal of Applied Research and Technology, vol. 15, no. 6, pp. 562–570, Dec. 2017, doi: 10.1016/j.jart.2017.07.004.
M. C. Vuran, A. Salam, R. Wong, and S. Irmak, ‘Internet of underground things in precision agriculture: Architecture and technology aspects’, Ad Hoc Networks, vol. 81, pp. 160–173, Dec. 2018, doi: 10.1016/j.adhoc.2018.07.017.
A. L. González, J. A. T. García, and K. R. V. Romero, ‘JAKEBOT: IoT Based and Machine Learning Water Quality Monitoring for Rivers.’.
M. Rezaei, M. A. Moghaddam, J. Piri, G. Azizyan, and A. A. Shamsipour, ‘Drought prediction using advanced hybrid machine learning for arid and semi-arid environments’, KSCE Journal of Civil Engineering, vol. 29, no. 4, p. 100025, Apr. 2025, doi: 10.1016/j.kscej.2024.100025.
S. Calengor, S. P. Katragadda, and J. Steier, ‘Adversarial Threats in Climate AI: Navigating Challenges and Crafting Resilience’, Proceedings of the AAAI Symposium Series, vol. 2, no. 1, Art. no. 1, 2023, doi: 10.1609/aaaiss.v2i1.27648.
C. Murphy and H. Meresa, ‘HydroPredict: Ensemble River Flow Scenarios for Climate Change Adaptation’. Accessed: Apr. 21, 2025. [Online]. Available: https://www.epa.ie/publications/research/climate-change/Research_Report-453.pdf
H. M. Forhad et al., ‘IoT based real-time water quality monitoring system in water treatment plants (WTPs)’, Heliyon, vol. 10, no. 23, p. e40746, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40746.
C. Parra-López et al., ‘Digital technologies for water use and management in agriculture: Recent applications and future outlook’, Agricultural Water Management, vol. 309, p. 109347, Mar. 2025, doi: 10.1016/j.agwat.2025.109347.
S. Hashemipour and M. Ali, ‘Amazon Web Services (AWS) – An Overview of the On-Demand Cloud Computing Platform’, in Emerging Technologies in Computing, M. H. Miraz, P. S. Excell, A. Ware, S. Soomro, and M. Ali, Eds., Cham: Springer International Publishing, 2020, pp. 40–47. doi: 10.1007/978-3-030-60036-5_3.
P. Borra, ‘The Evolution and Impact of Google Cloud Platform in Machine Learning and AI’, Jun. 18, 2024, Social Science Research Network, Rochester, NY: 4914163. Accessed: Apr. 21, 2025. [Online]. Available: https://papers.ssrn.com/abstract=4914163
Al-Qaisi, ‘Smart Water Systems: The Role of Technology and Engineering in Optimizing Urban Water Resources’, ResearchGate, Mar. 2025, doi: 10.52783/jisem.v10i21s.3445.
Y. Wang and S. Razmjooy, ‘Prediction of drought hydrological and water scarcity based on optimal artificial intelligence by developing a metaheuristic optimization algorithm’, Physics and Chemistry of the Earth, Parts A/B/C, vol. 135, p. 103669, Oct. 2024, doi: 10.1016/j.pce.2024.103669.
D. O. 1 Perez, K. 1 Marceles, E. V. 1 Palta, and G. E. G. 2 1 I. U. C. M. del C. Chanchi, ‘Sistema de riego con tecnología IoT: Smart Drip System’, pp. 121–133, 2019, Accessed: Apr. 25, 2025. [Online]. Available: https://www.proquest.com/docview/2348878035?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals
J. Chen, X. Wei, Y. Liu, C. Zhao, Z. Liu, and Z. Bao, ‘Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir’, Applied Sciences, vol. 14, no. 19, Art. no. 19, Jan. 2024, doi: 10.3390/app14198755.
