Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R² values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R² values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

The China-Pakistan Economic Corridor (CPEC) is an ongoing mega-construction project in Pakistan that necessitates further exploration of new natural resources of aggregate to facilitate the extensive construction. Therefore, the Late Permian strata of Chhidru and Wargal Limestone for aggregates resources were envisaged to evaluate their optimal way of construction usage through detailed geotechnical, geochemical, and petrographic analyses. Geotechnical analysis was performed under BS and ASTM standards with the help of employing different laboratory tests. A simple regression analysis was employed to ascertain mutual correlations between physical parameters. Based on the petrographic analysis, the Wargal Limestone is classified into mudstones and wackestone, and Chhidru Formation is categorized into wackestone and floatstone microfacies, both containing primary constituents of calcite and bioclasts. The geochemical analysis revealed that the Wargal Limestone and Chhidru Formation encompass calcium oxide (CaO) as the dominant mineral content. These analyses also depicted that the Wargal Limestone aggregates bear no vulnerability to alkali-aggregate reactions (AAR), whereas the Chhidru Formation tends to be susceptible to AAR and deleterious. Moreover, the coefficient of determination and strength characteristics, for instance, unconfined compressive strength and point load test were found inversely associated with bioclast concentrations and directly linked to calcite contents. Based on the geotechnical, petrographic, and geochemical analyses, the Wargal Limestone proved to be a significant potential source for both small and large-scale construction projects, such as CPEC, but the Chhidru Formation aggregates should be used with extra caution due to high silica content.

The China-Pakistan Economic Corridor (CPEC) is a massive in-progress construction project in Pakistan that connects more than 70 countries via multiple trade channels such as highways, railways, roads, and fiber optics. This project also involves the development of local infrastructure and industrial zones in Pakistan, which demands the discovery of new resources of aggregate to facilitate the construction. Therefore, physical characterization research was carried out on the Kirman hill region (Jurassic limestone), District Kurram, Pakistan, to investigate their suitability for utilization as construction materials using site investigation and laboratory studies. The results outline that all typical engineering parameters are within acceptable limits set by international standards like BS, ASTM, and AASHTO. Bituminous tests revealed that Jurassic limestone is appropriate as an aggregate for asphalt wearing coarse. Likewise, the petrographic study performed shows proper matching with engineering tests. The petrographic analysis of Jurassic limestone showed a minute amount of deleterious content; as a result, it is resistant to Alkali silica reaction (ASR) and Alkali carbonate reaction (ACR) expansions. Based on engineering and petrographic analysis, the Jurassic limestone, Kirman hill region, District Kurram, Pakistan is recommended as a potential aggregate for (i.e., base course, subbase course, cement concrete, and asphalt) and other mega and minor civil construction projects. Doi: 10.28991/CEJ-2022-08-03-02 Full Text: PDF

With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.