Technology Applied to Identifying Areas without Access to Drinking Water: Challenges and Opportunities
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i3.1358Keywords:
Areas without drinking water, Applied technology, Main components, Artificial Intelligence, Public policies for drinking water supply, Zonas sin agua potable, Tecnología aplicada, Inteligencia artificialAbstract
Safe access to drinking water remains a critical priority for governments and international organizations. The World Health Organization estimates that more than 2.2 billion people lack safely managed water services, which has serious consequences for health and economic development. In this context, different technologies, and especially artificial intelligence (AI), are emerging as innovative tools that can help identify, more accurately and quickly, areas with inadequate drinking water service coverage. (WHO & UNICEF, 2021)
This study analyzes the applications of machine learning and statistical techniques to identify sectors of society with a lack of drinking water supply. It also describes methodological approaches that integrate sociodemographic, environmental, and water infrastructure data with predictive models based on neural networks and decision trees. (Mutiara, Andriani, & Rahadi, 2021)
Finally, the challenges associated with data quality, the replicability of models in different contexts, and the need for public policies that promote the responsible adoption of these cutting-edge technologies are discussed. (Shahriar, Rahman, & Islam, 2023).
Spanish-language metadata / Metadatos en español
Título en español:
La tecnología aplicada a la identificación de zonas sin acceso al agua potable: retos y oportunidades
Resumen:
El acceso seguro al agua potable sigue siendo una prioridad fundamental para los gobiernos y las organizaciones internacionales. La Organización Mundial de la Salud calcula que más de 2.200 millones de personas carecen de servicios de agua gestionados de forma segura, lo que tiene graves consecuencias para la salud y el desarrollo económico. En este contexto, diversas tecnologías, y especialmente la inteligencia artificial (IA), se están perfilando como herramientas innovadoras que pueden ayudar a identificar, con mayor precisión y rapidez, las zonas con una cobertura insuficiente del servicio de agua potable. (OMS y UNICEF, 2021). Este estudio analiza las aplicaciones del aprendizaje automático y las técnicas estadísticas para identificar los sectores de la sociedad que carecen de suministro de agua potable. Además, describe enfoques metodológicos que integran datos sociodemográficos, ambientales y de infraestructura hídrica con modelos predictivos basados en redes neuronales y árboles de decisión. (Mutiara, Andriani y Rahadi, 2021). Por último, se analizan los retos relacionados con la calidad de los datos, la replicabilidad de los modelos en diferentes contextos y la necesidad de políticas públicas que promuevan la adopción responsable de estas tecnologías de vanguardia (Shahriar, Rahman e Islam, 2023).
Palabras Claves:
Zonas sin agua potable, Tecnología aplicada, Componentes principales, Inteligencia artificial, Políticas públicas para el abastecimiento de agua potable
Smart citations:
https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1358
Dimensions.
Open Alex.
References
Abdulameer, L., Al Maimuri, N. M. L., Nama, A. H., Rashid, F. L., & Al-Dujaili, A. N. (2025). The role of artificial intelligence in managing sustainable water resources: A review of smart solution implementations. Water Conservation & Management, 9(2), 181–191. https://doi.org/10.26480/wcm.02.2025.281.291
Bain, R., Cronk, R., Hossain, R., Bonjour, S., Onda, K., Wright, J., Yang, H., Slaymaker, T., Hunter, P., Prüss-Ustün, A., Bartram, J., & Fewtrell, L. (2014). Global assessment of exposure to faecal contamination through drinking water based on a systematic review. Tropical Medicine & International Health, 19(8), 917–927. https://doi.org/10.1111/tmi.12334
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in Python. Journal of Machine Learning Research, 14, 2349–2353.
Javanbakht-Sheikhahmad, F., Rostami, F., Azari, A., & Veisi, H. (2025). Mapping and managing water poverty indicators: A spatio-temporal analysis. Environmental and Sustainability Indicators, 28, Article 101005. https://doi.org/10.1016/j.indic.2025.101005
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), Article 20150202. https://doi.org/10.1098/rsta.2015.0202
Kunz, R., Eisenberg, J. N. S., Hubbard, A., & Colford, J. M. (2007). The contribution of epidemiology to risk assessment: Drinking water and intestinal protozoa. Risk Analysis, 27(2), 355–374. https://doi.org/10.1111/j.1539-6924.2007.00887.x
Li, J., Ma, R., Cao, Z., Xue, K., Xiong, J., Hu, M., & Feng, X. (2022). Satellite Detection of Surface Water Extent: A Review of Methodology. Water, 14(7), Article 1148. https://doi.org/10.3390/w14071148
MacDonald, A. M., & Calow, R. C. (2009). Developing groundwater for secure rural water supplies in Africa. Desalination, 248(1–3), 546–556. https://doi.org/10.1016/j.desal.2008.05.100
Sang, J., Hou, B., Wang, H., & Ding, X. (2023). Prediction of water resources change trend in the Three Gorges Reservoir Area under future climate change. Journal of Hydrology, 617, Article 128881. https://doi.org/10.1016/j.jhydrol.2022.128881
World Health Organization & United Nations Children’s Fund. (2021). Progress on household drinking water, sanitation and hygiene 2000–2020: Five years into the SDGs. WHO/UNICEF Joint Monitoring Programme. https://www.who.int/publications/i/item/9789240030848
Ye, L., Dong, Q., McCright, A., & Gasteyer, S. (2026). An Innovative Approach to Predict Drinking Water Risks Using System, Community, and Regulatory Characteristics. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 63. https://doi.org/10.1177/00469580251411440
Yitageasu, G., Chekole Adane, K., Hailu Tesfaye, A., Gizaw, Z., Kifle, T., & Demoze, L. (2025). Geospatial analysis of limited access to drinking water services in East African countries: Insights from recent demographic and health surveys. Frontiers in Water, 7, Article 1487795. https://doi.org/10.3389/frwa.2025.1487795
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
