Luis Carlos Gonzalez

Water losses in Water Distribution Networks remain a persistent challenge, with global impacts exceeding $39 billion annually. While Artificial Intelligence (AI) offers promising leak detection solutions, assessing operational readiness and reproducibility is difficult due to literature fragmented across sensing technologies and spatial scales. This systematic review of 86 studies (2015–2026) structures the field through a hierarchical hydroinformatics framework: network-level monitoring, district metered area analysis, and In-situ localization. Each level relies on distinct sensing modalities conditioning feature engineering and model selection. Crucially, the analysis reveals that the primary bottleneck preventing real-world deployment is not algorithmic sophistication, but the prevalence of isolated computational approaches that ignore physical and operational constraints. To bridge the gap between simulated validation and operational utility, this review argues for conceptualizing AI as a sensor-aware, adaptive hierarchical mechanism. Furthermore, it exposes the urgent need for a paradigm shift in evaluation standards by moving beyond traditional metrics to explicitly incorporate operational False Alarm Rates, computational feasibility, and integration with operational systems (SCADA/GIS). By aligning algorithmic evaluation with infrastructural realities, this critical assessment establishes how AI-based approaches must evolve to become a viable tool for water loss reduction.

La pérdida de agua por fugas en las redes de distribución representa uno de los mayores desafíos para el manejo sostenible de este recurso. En países en desarrollo, se estima que más del 40 % del agua enviada a la red se desperdicia, mientras que en algunas ciudades del estado de Chihuahua estas pérdidas superan el 50 %. Ante este panorama, la inteligencia artificial (IA), una tecnología capaz de identificar patrones y generar información útil a partir de grandes cantidades de datos, surge como una herramienta prometedora para mejorar la detección de fugas. Este artículo explora cómo la IA puede aprovechar la información recopilada por los sistemas de monitoreo de las redes hidráulicas para localizar fallas con mayor precisión, reducir el desperdicio de agua y optimizar la gestión de los recursos hídricos. Además, presenta ejemplos de estrategias innovadoras que combinan modelos digitales y aprendizaje automático para enfrentar este problema. Comprender estas tecnologías permite vislumbrar nuevas oportunidades para proteger un recurso esencial para la sociedad y avanzar hacia un uso más eficiente y sustentable del agua.

Insurance Telematics is a recent service that is offered to drivers where a sensing platform measures vehicle’s dynamics with the ultimate goal of inferring the level of risk that the driver experiences on the road. For this to happen, a robust set of sensors must be deployed within the vehicle and effective algorithms to process and classify driving patterns must be employed. As feasible as it looks, however, insurance telematics poses a great challenge when sensors offer information that is redundant or even show a low signal/noise ratio. The question here, then, is how to identify what sensing information is really useful and discard that one that only adds overhead. In this study we proposed an algorithmic approach, called minimal Gated Multimodal Unit (mGMU) that learns to filter out the proper amount of data from different input sources, making it an appealing choice for data fusion scenarios. We evaluate mGMU on three public multi modal (multiple sensor) datasets, created to classify driving styles, that comprise 3, 3 and 8 sensor sources. After a very extensive experimental setup that comprised around 30 different pipelines (made of feature extractors, information fusion approaches and a classification layer), results show that configurations based on mGMU obtain the best performance (with an increase of 2% with respect the second best approach and even outperforming the use of AutoEncoders by 5%). Moreover, as a proof of concept we deployed mGMU on three commercial smartphones, thus showing its efficiency given the number of parameters and FLOPS that were dramatically reduced. Given these results, we expect mGMU to be of great use on resource-constrained edge computing devices and rich sensor scenarios where data from different data sources must be exploited.

As a first step towards decreasing greenhouse gas emissions originating from transportation, it is critical that we create efficient systems for monitoring individual travel patterns and the associated carbon footprints. To this end, this paper presents a CO ₂ e emission estimator that combines transportation mode classification with mode-specific emissions data. In addition to assessing the accuracy of the final emission estimation, we also categorize error sources and discuss their relative importance. Finally, we provide recommendations for designers of future carbon footprint estimators. Experimental results support the notion that transportation mode classifiers used for carbon footprint estimation should be evaluated based on their ability to identify carbon emitting transportation modes, while giving lower priority to recognition of various stationary activities and low-emission transportation modes. Additionally, it is demonstrated that errors in the estimated traveled distance have a low impact on the overall emissions error compared to errors in the transportation mode classification or in the assumed emissions per traveled distance for a specific mode.