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This study focused on the significance of underground mining in Pakistan, resulting in the employment of operational staff to undertake the primary tasks of this sector, such as explosions, rock excavation, mineral research, mining-supporting walls, and mine compactivity. Occupational accidents and illnesses arise due to the activities mentioned above because the working circumstances are not optimal. The decision-matrix risk-assessment (DMRA) approach, in which incidents are evaluated according to their severity and probability, was also utilized to improve working conditions, including public health and environment protection. To assess the risks and to select which actions should continue in the same manner, we highlighted hazards that need control measures and, as the last option, those that must be stopped. By taking into account the results of the study, corrective actions were proposed that can help avoid the occurrence of the presented accidents through applying occupational safety and health regulations issued by the Department of Minerals and Mines, which is a governmental entity responsible for both the issuing and the compliance to those regulations. The current study also outlined the requirements that must be reported under mining-related laws.

The Upper Indus Basin, in Pakistan’s western Salt Range, is home to the Zaluch Gorge. The sedimentary rocks found in this Gorge, belonging to the Chhidru Formation, were studied in terms of sedimentology and stratigraphy, and provide new insights into the basin paleogeographic evolution from the Precambrian to the Jurassic period. Facies analysis in the Chhidru Formation deposits allowed the recognition of three lithofacies (the limestone facies—CF1, the limestone with clay interbeds facies—CF2, and the sandy limestone facies—CF3) with five microfacies types (mudstone biomicrite—MF-1, wackestone-biomicrite—MF-2, wackestone-biosparite—MF-3, pack-stone-biomicrite—MF-4, and packstone-biosparite—MF-5), as well as their depositional characteristics. The identified carbonate and siliciclastic formations display various facies in a shallow marine environment, with different lithologies, sedimentary features, and energy conditions. It is thought that the depositional characteristics of these microfacies are closer to those of the middle to outer shelf. Because of the progressively coarsening outcrop sequence, this formation seems to be at the very top of the high stand system tract (HST). A modified dynamic depositional model of the Chhidru Formation is further built using outcrop data, facies information, and stratigraphy. According to this concept, the formation was deposited in the middle to inner shelf area of the shallow marine environment, during the Late-Permian period. The Permo-Triassic Boundary (PTB), which is the end of the type-1 series, is marked by this formation’s top.

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Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan’s Tarbela dam, the world’s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN–LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions.