Mohammed Sazid

This study evaluates and compares the performance of machine learning (ML) algorithms, namely Random Forest (RF), Logistic Regression (LR), Decision Tree (DT) and Multilayer Perceptron (MLP) for delineating high-resolution landslide hazard and risk maps along a section of National Highway-9 (NH-9) in Uttarakhand, India. A total of fifteen landslide conditioning factors (LCFs) were selected with special emphasis on field based geotechnical and bedding orientation relative to slope were incorporated. Multicollinearity analysis verified variable independence and reliability and the models were trained on a balanced dataset of 95 landslide and non-landslide locations. The limited spatial extent of high and very high hazard zones indicates localized instability, implying that risk mitigation efforts can be strategically planned and focused on discrete vulnerable sections. Model performance was assessed through Receiver Operating Characteristic (ROC) curve, Precision-Recall, accuracy, F1 score and Kappa coefficient. The ROC analysis indicated that all four ML models achieved excellent discrimination (AUC > 0.90) along with validation from other metrices. Among them, the RF model exhibited the best overall performance, with an AUC of 96.8% for the training dataset (Success Rate Curve) and 96.0% for the testing dataset (Prediction Rate Curve), indicating excellent predictive capability and strong generalization. Although the DT model achieved the second highest testing AUC (95.1%), its lower training AUC (94.1%) suggests slightly less consistent performance across datasets. The MLP and LR models also demonstrated good predictive performance but were marginally inferior to the RF and DT models. This study further extends the applicability of landslide hazard outputs by generating landslide risk maps through the integration of elements at risk and their vulnerability. Population and LULC were utilized as exposure elements for risk zonation mapping. The outputs highlight the effectiveness of ensemble ML techniques in landslide studies providing insights for hazard mitigation, infrastructure planning and sustainable development along this Himalayan transportation corridor.

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field.

Underground mining activities have potential influence on the surface slope stability. However, suitable attention has not yet been paid to these problems. The aim of this study is to analyze the possible influence of present and former mining activities on the selected set of physical and numerical model slope deformations in the Jharia Coalfield. In India coking coal seams are mainly confined to the Jharia coalfields of Jharkhand State. There are 19 coal seams, which were partially exploited earlier adopting the underground mining method. However, due to various complex geotechnical problems, it was not economically viable to continue the same method for coal extraction. A vast amount of coking coal remains unrecovered as they stand as pillars, collapsed materials, partings, barriers, etc. waiting to be extracted to meet the growing energy demand. In order to bridge the gap between the growing demands and supply, it is essential that the coal locked in the underground workings be recovered fully by implementing surface mining methods. As these new surface mines are planned in the areas which contain old surface and underground workings, the major problems will be the stability of slopes particularly when they meet the old underground workings, and the behavior of previously subsided material in the pit slopes. The present study was conducted to understand the effect of underground workings on the stability of new slopes using physical and numerical simulation. This paper emphasizes on the numerical simulation and further verification of the physical model results. The physical model results are well corroborated with numerical simulation study.

Drilling and blasting remain the preferred technique used for rock mass breaking in mining and construction projects compared to other methods from an economic and productivity point of view. However, rock mass breaking utilizes only a maximum of 30% of the blast explosive energy, and around 70% is lost as waste, thus creating negative impacts on the safety and surrounding environment. Blast-induced impact prediction has become very demonstrated in recent research as a recommended solution to optimize blasting operation, increase efficiency, and mitigate safety and environmental concerns. Artificial neural networks (ANN) were recently introduced as a computing approach to design the computational model of blast-induced fragmentation and other impacts with proven superior capability. This paper highlights and discusses the research articles conducted and published in this field among the literature. The prediction models of rock fragmentation and some blast-induced effects, including flyrock, ground vibration, and back-break, were detailed investigated in this review. The literature showed that applying the artificial neural network for blast events prediction is a practical way to achieve optimized blasting operation with reduced undesirable effects. At the same time, the examined papers indicate a lack of articles focused on blast-induced fragmentation prediction using the ANN technique despite its significant importance in the overall economy of whole mining operations. As well, the investigation revealed some lack of research that predicted more than one blast-induced impact.