A labyrinth weir allows for higher discharge capacity than conventional linear weirs, especially at low hydraulic heads. In fact, this is an alternative for the design or rehabilitation of spillways. It can even be used as a strategy in problems related to dam safety. A sequential design method for a labyrinth weir is based on optimal geometric parameters and the results of discharge flow analysis using Computational Fluid Dynamics and the experimental studies reported in the literature. The tests performed were for weirs with values of H_T/P ≤ 0.8 and for angles of the cycle sidewall of 6° ≤ α ≤ 20°. The results of the discharge coefficient are presented as a family of curves, which indicates a higher discharge capacity when H_T/P ≤ 0.17. Four aeration conditions are identified with higher discharge capacity when the nappe is adhering to the downstream face of the weir wall and lower discharge capacity when the nappe is drowned. Unstable flow was present when 12° ≤ α ≤ 20°, with a greater presence when the nappe was partially aerated and drowned. The interference of the nappe is characterized and quantified, reaching up to 60% of the length between the apex, and a family of curves is presented as a function of H_T/P in this respect. Finally, a spreadsheet and a flowchart are proposed to support the design of the labyrinth type weir.

Mining activities are conducted to extract valuable minerals from the Earth, which are used to manufacture many objects. However, these operations generate landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, landform changes were monitored in a mining area in Mazapil, Zacatecas, Mexico, using geomatic techniques. Multitemporal Landsat satellite images and digital elevation models (DEMs) from different years were used to detect and quantify landform alterations and estimate the volumes of removed material. The results show ground depressions greater than −333 m and waste material accumulations greater than +152 m, with an average standard deviation of ±3.6 m. A total excavation volume of 413.524 million m³ and a total fill volume of 431.194 million m³ were quantified, with an estimated standard deviation of ±810 m³. The proposed methodology proved effective for the remote quantification of large-scale relief disturbances in open-pit mining areas. It can also be used for environmental monitoring and hydrological risk assessment in active and inactive mining areas.

Leak localization and maintenance in water distribution networks (WDNs) are essential for reducing water losses and operating costs; however, they usually require extensive monitoring and large datasets. This work proposes a methodology that combines topological sectorization of a hydraulic node network and deep learning techniques to improve leak location by selecting representative nodes to reduce the spatial dimensionality of the WDNs. The network is partitioned using a Spectral Clustering algorithm to identify key nodes based on a weighted criterion that considers pressure variability, flow rate, and proximity to the centroid. Subsequently, a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network classifies the cluster and sub-cluster where a leak occurs, using pressure and flow time series simulated in EPANET. This methodology was validated on the L-Town network, achieving an accuracy of 99.94% for cluster classification and 99.82% for sub-clusters, with a validation loss of 0.024%. During validation with 117 unseen leakage scenarios, the model reached an overall effectiveness of 85%. Moreover, Spectral Clustering outperformed K-Means in preserving physical connectivity. These results confirm the efficiency of the proposed methodology and highlight its potential for application in other hydraulic networks.

Mining activities are conducted to extract valuable minerals from the Earth, used in the manufacture of many objects. However, these operations have been associated with significant landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, remote sensing (RS) and geographic information systems (GIS) techniques were applied to verify landform changes induced by open-pit mining in Mazapil, Zacatecas, Mexico. Multi-temporal Landsat 5 and 8 satellite images, along with digital elevation models (DEMs) from 1998 and 2014, were used to detect variations in land cover and terrain shape. Analyses revealed ground depressions greater than −333 m and waste accumulations greater than +152 m, with an average standard deviation of ±3.6 m. A total excavation volume of 413,524,124 m³ and an embankment volume of 431,194,785 m³ were quantified, with an estimated standard deviation of ±810 m³. Despite limitations related to DEM resolution, data availability, and restricted mine access, the proposed method proved effective for the remote quantification of large-scale topographic alterations in open-pit mining areas. The integration of satellite imagery, multi-temporal DEMs, and geospatial analysis constitutes a cost-effective and replicable methodology for environmental monitoring, landscape modeling, and hydrological risk assessment in both active and post-mining environments.