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Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, and machine learning (ML), along the main road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performance matrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelum road is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslide-prone zones along road corridors.

Several devastating landslides have occurred in the NW Himalayas, which has prompted several researchers to strive for improvement in landslide susceptibility modelling (LSM) methodologies. This research analyzes the effectiveness of alternative landslide partitioning techniques on LSM in the landslide-prone district, Muzaffarabad, Pakistan. We developed a landslide inventory of 961 landslides and then traditionally divided it into training (672; 70%) and testing (289; 30%) samples. These training samples (672) are processed by the Average Nearest Neighbour Index (ANNI) method to estimate the spatial pattern of landslides in nature. The results provide an ANNI ratio of 0.672 confirming that the landslides distribution pattern is cluster in the complex Himalayan terrain of Muzaffarabad. Among 672, the majority of landslides (529; 79%) depict cluster behaviour, while 189 landslides (21%) depict random behaviour. To evaluate the effectiveness of landslide cluster samples in prediction, five machine learning algorithms (MLAs), that is, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) using proposed cluster (529) and traditional (672) random training samples along with 17 geo-environmental factors are executed. However, testing samples (289; 30%) separated at the initial stage remained the same to check the model's effectiveness. The areas under the curve (AUC-ROC), sensitivity, specificity, Kappa index and accuracy (ACC) have been used to evaluate the MLA's performances. An alternative partitioning technique (cluster) shows the highest predictive power with AUC-ROC values ranging from 0.96 to 0.86, Kappa index ranges from 0.76 to 0.60 and ACC ranges from 0.90 to 0.83. Conversely, the random partitioning approach performs less well with AUC-ROC values ranging from 0.95 to 0.83, Kappa index ranges from 0.70 to 0.49 and ACC ranges from 0.87 to 0.80. In comparison, the RF cluster sampling-based model outperforms the other models and their counterparts. The RF model achieved the highest accuracy (0.902), highest AUC (0.962) and highest Kappa index (0.755) followed by XGboost having ACC (0.885), AUC (0.95) and Kappa index (0.724) employing proposed cluster training samples. However, traditional random training samples yield comparatively low ACC of RF (0.868) and XGboost (0.862). These results confirm that cluster training sampling performs well in obtaining reliable and precise LSMs for complex Himalayan terrain. Although random landslide partitioning for training datasets is seldom utilized in LSM, this study highlights that cluster partitioning for landslide training datasets might be a realistic and reliable approach. [ABSTRACT FROM AUTHOR]

Machine learning methods are considered as most effective approaches to accomplish landslide susceptibility analysis around the globe. Landslide susceptibility maps (LSMs) have been frequently executed by statistical models in NW Himalaya. However, the comparison and applications of the statistical models with modern machine learning techniques has not been fully explored in this region. Hence, this study aims to compare the predicted performance of statistical and popular machine learning models to explore robust landslide prediction model in the landslide-prone area of NW Himalaya and investigate the compensations and limitations of these models to grasp a more precise and consistent result. This study presented machine learning approaches based on the artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) and the statistical methods based on the frequency ratio (FR), information value (InfoV) and weight of evidence (WoE). For this purpose, first an inventory map of 1507 landslides was prepared and randomly divided into training (70%) and testing (30%) dataset. Furthermore, 12 landslide conditioning factors (LCFs) were extracted from geospatial dataset to prepare thematic layers in ArcGIS. Thereafter, factor analysis was performed to eliminate colinear and least important variables which can mislead the results. The results showed that all selected LCFs are noncolinear and have significant contribution on landslides initiation, however, lithology, slope angle, annual rainfall and landuse were most influential factors. For modeling purpose, landslide inventory was correlated against all LCFs and trained into six models to produce respective LSMs. Finally, the performance of produced LSM models was validated and compared through area under receiver operating characteristic curve (AUROC), Accuracy, Recall, F1-score and Cohen's Kappa coefficients to assess the robustness of employed models. The results exhibit that the performance scores of machine learning models were considerably superior than statistical models. While, the AUROC values based on validation dataset indicate that LR (0.89) has better prediction ability followed by SVM (0.86), ANN (0.84), FR (0.83), InfoV (0.82) and WoE (0.81) in this study. Therefore, it is reasoned out that the machine learning methods are more reliable in generating adequate LSMs. However, the LR is recommended as most efficient model for predicting landslide susceptible zones in study region and thus can be considered as robust model for landslide susceptibility assessment in similar geo-environmental regimes. [ABSTRACT FROM AUTHOR]

To investigate the sedimentological characteristics of the Margalla Hill Limestone (MHL), a multi‐proxy approach including geochemical, mineralogical, and micropalaeontological investigations were carried out. Using detailed field observations and petrographic investigation of the studied section, five microfacies were dentified. Total organic carbon (TOC) values of the limestone are generally low (0.10–0.96%) and highly degraded, as observed from the low hydrogen index (i.e., 17–349 mg HC/g TOC). TOC is mainly Type‐III and Type‐IV organic matter that reflects a continental source or highly weathered marine organic matter (OM) with minor Type‐II which corresponds to algal or bacterial OM. The values of Tmax range from 419 to 512°C for most of the samples, which lies within the mature stage. Major element abundances do not vary significantly in the studied unit and are mainly linked to variations in clay contents. Trace elements (V, Cr, Mn, Zn, Ni, Cu, Th, Rb, U, and Zr) of the MHL depict a mainly detrital origin for shales, and overall decrease in trace element's concentration suggests a change from physical to chemical weathering. The rate of sedimentation as determined by the Th/U ratio indicates a regular to irregular low rate of sedimentation, from base to top of the studied section. The redox conditions, as reflected in the variation of Cr (V/Cr), using the V/Cr ratio with Ni + V indicate that the MHL was deposited under oxic‐dysoxic conditions. Chemical Index of Alteration, Chemical Index of Weathering, Plagioclase Index of Alteration, and Weathering Index of Parker showed that the source region was subjected to intense chemical weathering likely under tropical palaeoclimatic conditions with abundant rainfall. The limestone consists mainly of calcite followed by minor quartz and dolomite. Clay mineralogy revealed that the clay fraction is dominated by kaolinite, smectite, mica, and chlorite. The microfossils of various genera of benthic foraminifera have been identified. Based on benthic foraminifera, an Early Eocene (SBZ‐8; Middle Ilrdian 2) age has been assigned to the MHL. [ABSTRACT FROM AUTHOR]

The use of marble as a building or dimension stone is one of the most growing commodities throughout the world. Due to the rapid increase in urbanization, dimension stones are commonly used in building interiors as well as exteriors. Marble, being a metamorphic rock is very desirable to be utilized in structural as well as decorative purposes. The study presented herein evaluates the geotechnical characteristics of the Gahirat marble in the Chitral area, Pakistan. For this purpose, 40 representative samples of the Gahirat marble from ten different vicinities of the Chitral area were collected and tested to determine the correlation amongst the different geotechnical properties of the studied rock. The uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), water absorption, specific gravity, point load strength (PLS), Schmidt rebound hammer, ultrasonic pulse velocity (UPV), and soundness, tests were performed to evaluate the strength of the Gahirat marble for its possible use as a dimension and building stone. An average of 12 measurements for each of the ten rock sample collection vicinities were performed. The statistical analysis shows that a fairly strong linear positive correlation (R2 = 0.80, 0.81, 0.84, 0.92) exists between UCS along with BTS, specific gravity, water absorption PLS and UPV. The maximum values obtained for the 12 measurements regarding the mechanical properties such as UCS, BTS, PLS and Schmidt hammer rebound number were 97.25 MPa, 11.34 MPa, 6.75 MPa and 51, respectively. The maximum values of the physical properties such as water absorption, soundness, specific gravity and UPV tests were calculated to be 0.08%, 0.38, 2.68 and 5.04 km/s, respectively. The results of the uniaxial compressive strength, Brazilian tensile strength, point load strength and Schmidt hammer rebound tests indicate that the Gahirat marble shows high resistance to crushing and bending effects while the specific gravity values indicate its ability to bear the impact of the degree of polishing and grinding. The water absorption and soundness test values indicated that the Gahirat marble is appropriate to be utilized for floor tiles and outdoor cladding due to its resistance against weathering and low rate of the water absorption. [ABSTRACT FROM AUTHOR]

The use of marble as a building or dimension stone is one of the most growing commodities throughout the world. Due to the rapid increase in urbanization, dimension stones are commonly used in building interiors as well as exteriors. Marble, being a metamorphic rock is very desirable to be utilized in structural as well as decorative purposes. The study presented herein evaluates the geotechnical characteristics of the Gahirat marble in the Chitral area, Pakistan. For this purpose, 40 representative samples of the Gahirat marble from ten different vicinities of the Chitral area were collected and tested to determine the correlation amongst the different geotechnical properties of the studied rock. The uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), water absorption, specific gravity, point load strength (PLS), Schmidt rebound hammer, ultrasonic pulse velocity (UPV), and soundness, tests were performed to evaluate the strength of the Gahirat marble for its possible use as a dimension and building stone. An average of 12 measurements for each of the ten rock sample collection vicinities were performed. The statistical analysis shows that a fairly strong linear positive correlation (R2 = 0.80, 0.81, 0.84, 0.92) exists between UCS along with BTS, specific gravity, water absorption PLS and UPV. The maximum values obtained for the 12 measurements regarding the mechanical properties such as UCS, BTS, PLS and Schmidt hammer rebound number were 97.25 MPa, 11.34 MPa, 6.75 MPa and 51, respectively. The maximum values of the physical properties such as water absorption, soundness, specific gravity and UPV tests were calculated to be 0.08%, 0.38, 2.68 and 5.04 km/s, respectively. The results of the uniaxial compressive strength, Brazilian tensile strength, point load strength and Schmidt hammer rebound tests indicate that the Gahirat marble shows high resistance to crushing and bending effects while the specific gravity values indicate its ability to bear the impact of the degree of polishing and grinding. The water absorption and soundness test values indicated that the Gahirat marble is appropriate to be utilized for floor tiles and outdoor cladding due to its resistance against weathering and low rate of the water absorption. [ABSTRACT FROM AUTHOR]

Geological units of sub-Himalaya Pakistan lying in the core of Hazara Kashmir Syntaxial bend provide vast potential for concrete aggregates in the region. The selection of suiTab. aggregate within active tectonic, fragile lithologic and geotechnical environments is a challenging approach. The present study focused on evaluating the physico-mechanical properties of crushed rock aggregates (CRA) that are already being mined in Muzaffarabad and surrounding areas. For this purpose, integrated physico-mechanical with combined petrographic and mineralogical analysis was performed on dolomite and dolomitic limestone of the Cambrian Muzaffarabad Formation and Eocene Margalla Hill Limestone of Yadgar section. Therefore, samples of aggregates were collected from different quarries in accordance with ASTM standards and their mechanical and engineering properties were evaluated through ASTM and BS standards. The physico-mechanical properties of dolomite and dolomitic limestone conclude that the dolomite rocks have the average strength against abrasion (25.12%), impact (20.75%) and crushing (19.57%), higher specific gravity (2.76), lower water absorption (0.65%) and higher value of sulphate soundness (1.5%). On the contrary, the Margalla Hill Limestone which exhibits average values of abrasion (16.93%), impact (22.03%), crushing (19.95%) with lower specific gravity (2.63), high water absorption (0.68%), and low values sulphate soundness (0.77%). The petrographic investigations showed that chert 3 - 6 % and granular quartz 25 - 30 % on average are also found in dolomite and dolomitic limestone, which causes the alkali-silica reaction (ASR) and alkali-carbonate reaction (ACR) when used in the concrete. The XRD analysis confirmed that both the formations dominantly contain the minerals dolomite, calcite and quartz. It was also found that Margalla Hill Limestone is more suiTab. to be utilised as coarse aggregate in concrete as compared to dolomite of the Muzaffarabad Formation. These exploitations allow recommending Eocene Margalla Hill Limestone of Yadgar section in the production of the coarse aggregate for concrete. [ABSTRACT FROM AUTHOR]

The rock units of the NW Himalayan region are fragile, heavily fractured and highly deformed due to active tectonics and complex geological setup. Fast urbanization, road constructions along hill slopes and other infrastructural development activities also increased the slopes instability problems. The present study emphasizes the application of rock mass classification to estimate the rock mass properties along the Yadgar section Muzaffarabad, NW Himalayas, Pakistan. For this purpose, Rock Mass Rating (RMR) and Geological Strength Index (GSI) were used to characterize and classify the rock masses. In the present study, twenty-five sites have been investigated to evaluate rock properties along the Muzaffarabad-Neelum road, Sub-Himalayas, Pakistan. Result of the study shows that the Abbottabad Formation of Cambrian age is vulnerable in the Yadgar section with extremely poor RQD (Rock Quality Designation), lowest UCS (Unconfined Compression Strength) values and closely spaced discontinuities. RMR values of the Abbottabad Formation ranges from 40-54 and classified as Poor to Fair having low GSI (20±3-35±3), blocky, disintegrated structure. The Paleocene Hangu Formation has lowest GSI (28±3-29±3; Blocky, Disturbed/Seamy in nature) having RMR (40-45) and Eocene Kuldana Formation has GSI (30±3- 45±3; Blocky) having RMR (34-67), are categorized as heavily broken, disintegrated and poorly interlocked rock masses. RMR values of rock units of the Paleocene Lockhart Formation (52-60), the Miocene Murree Formation (38-63), and the Eocene Margala Hill Limestone (38-61) are relatively higher values of GSI ranges from (35±3-45±3; 35±3-50±3; 30±3-40±3) respectively. RMR and GSI values in Yadgar section, ranges between 34-67 and 20±3-50±3 respectively. Analysis shows positive correlation between GSI and RMR values. This approach to evaluate the rock mass classification through RMR and GSI will give the better estimation of rock mass properties along Muzaffarabad-Neelum road to identify the vulnerable slopes and design effective geotechnical measures. [ABSTRACT FROM AUTHOR]

In order to get a higher success rate and a small number of wells for development, the reservoir characterization was carried out using seismic, Petrophysical and rock physics data in the B-interval of Miano 9 and 10, Miano area, Lower Indus Basin, Pakistan. For this purpose, at first, the wells and shot points of seismic lines were located on a base map. Then, the horizons were marked on seismic sections and further interpreted on the Time Depth (TD) charts. Moreover, a synthetic seismogram relaying the seismic and well data was made. In addition, different Petrophysical parameters were calculated using well logs and the geological properties of rocks were related with the corresponding elastic and seismic properties in analyzing the rock physics. The study indicates that the area is deformed by horst and graben structure in subsurface and lateral extents of faults were marked by fault polygons. The higher hydrocarbon saturation, porosity and permiability as determined through well logs and the low elastic properties calculated through rock physics in B-interval of Miano 9 and 10 indicate a good potential zone of Hydrocarbon. The finding of this research work is useful to understand the subsurface geology and hydrocarbon potential of the area. [ABSTRACT FROM AUTHOR]

Radon and its progenies found in water indicate the existence of seismically active faults in the region. However, exposure to high levels of radon can also result in radiation-related health risks. This study focuses on radon-based active tectonic studies along the Muzaffarabad Fault in the core of the Hazara-Kashmir Syntaxis (HKS), NW Himalayas, Pakistan. In this study, spring water samples were collected along roadside of Jhelum Valley and in close proximity to the Muzaffarabad Fault in Pakistan using Radon Thoron Monitor (RTM1688-2). The results of the study showed that the radon concentrations in the water samples ranged from 1.895 to 17.097 Bq/l. The study found that the highest radon concentration was observed in the samples collected closest to the fault, while the lowest concentration was observed in the samples collected further away. The statistical analysis between the radon concentration and the distance from the fault showed a strong inverse relationship (R 2 =0.73). The study also found that 68% of the sampling sites had radon concentrations that exceeded the maximum contamination level (MCL) set by the US Environmental Protection Agency (EPA). The higher radon concentrations in the springs water suggest the probability of earthquake, which in turn poses potential health risks for the local population. The findings suggest that the measurement of radon concentration in water can be used as a tool for identifying seismically active faults in the region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)