Vulnerability analysis in areas vulnerable to anthropogenic pollution has become a key element of sensible resource management and land use planning. This study is intended to estimate aquifer vulnerability using the DRASTIC model and using the vertical electrical sounding (VES) and electrical conductivity (EC) outcomes. The model allows for the identification of hydrogeological environments within the scope of the research, based on a composite definition of each environment’s main geological, geoelectrical, and hydrogeological factors. The results from the DRASTIC model were divided into four equal intervals, high, medium, low, and very low drastic index values. The SW area and NE area depict drastic index values from medium to very high, making it the most vulnerable zone in the study area, while the NW and SW areas show low to very low drastic index values. In addition, the results from the VES and EC the freshwater aquifer in the NE area and brackish water in the SE area, while the rest of the area falls into the category of brackish water. Overall, it can be concluded that areas having freshwater assemblages are on the verge of becoming contaminated in the future while the rest of the NW and SW areas constitute less vulnerable zones. The validation conducted for DRASTIC and EC shows a nearly positive correlation. Wastewater treatment policies must be developed throughout the studied region to prevent contamination of the remaining groundwater.

Landslides triggered in mountainous areas can have catastrophic consequences, threaten human life, and cause billions of dollars in economic losses. Hence, it is imperative to map the areas susceptible to landslides to minimize their risk. Around Abbottabad, a large city in northern Pakistan, a large number of landslides can be found. This study aimed to map the landslide susceptibility over these regions in Pakistan by using three Machine Learning (ML) techniques, specifically Linear Regression (LiR), Logistic Regression (LoR), and Support Vector Machine (SVM). Several influencing factors were used to identify the potential landslide areas, including elevation, slope degree, slope aspect, general curvature, plan curvature, profile curvature, landcover classification system, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), soil, lithology, fault density, topographic roughness index, and road density. The weights of these factors were calculated using ML techniques. The weightage overlay tool is adopted to map the final output. According to three ML models, lithology, NDWI, slope, and LCCS significantly impact landslide occurrence. The area under the ROC curve (AUC) is applied to validate the performance of models, and the results show the AUC value of LiR (88%) is better than SVM (86%) and LoR (85%) models. ML models and final susceptibility map gives good accuracy, which can be reliable for the results. The study’s outcome provides baselines for policymakers to propose adequate protection and mitigation measures against the landslides in the region, and any other researcher can adopt this methodology to map the landslide susceptibility in another area having similar characteristics.

Landslides are natural disasters deliberated as the most destructive among the others considered. Using the Muzaffarabad as a case study, this work compares the performance of three conventional Machine Learning (ML) techniques, namely Logistic Regression (LGR), Linear Regression (LR), Support Vector Machine (SVM), and two Multi-Criteria Decision Making (MCDM) techniques, namely Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the susceptibility mapping of landslides. Most of these techniques have been used in the region of Northern Pakistan before for the same purpose. However, this study for landslide susceptibility assessment compares the performance of various techniques and provides additional insights into the factors used by adopting multicollinearity analysis. Landslide-inducing factors considered in this research are lithology, slope, flow direction, fault lines, aspect, elevation, curvature, earthquakes, plan curvature, precipitation, profile curvature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), roads, and waterways. Results show that SVM performs better than LGR and LR among ML models. On the other hand, the performance of AHP was better than TOPSIS. All the models rank slope, precipitation, elevation, lithology, NDWI, and flow direction as the top three most imperative landslide-inducing factors. Results show 80% accuracy in Landslide Susceptibility Maps (LSMs) from ML techniques. The accuracy of the produced map from the AHP model is 80%, but for TOPSIS, it is less (78%). In disaster planning, the produced LSMs can significantly help the decision-makers, town planners, and local management take necessary measures to decrease the loss of life and assets.

Climber–abiotic parameter interactions can have important ramifications for ecosystem’s functions and community dynamics, but the extent to which these abiotic factors influence the spatial distributions of climber communities in the western Himalayas is unknown. The purpose of this study was to examine the taxonomic diversity, richness, and distribution patterns of climbers in relation to abiotic variables in the Jhelum District. The data were collected from 120 random transects between 2019 and 2021, from 360 sites within triplet quadrats (1080 quadrats), and classification and ordination analyses were used to categorize the sample transects. A total of 38 climber species belonging to 25 genera and 11 families were recorded from the study area. The Convolvulaceae were the dominant family (26.32%), followed by the Apocynaceae (21.05%), and Leguminosae (15.79%). The majority of the climbers were herbaceous in nature (71.05%), followed by woody (23.68%). Based on the relative density, the most dominant species was Vicia sativa (12.74). The majority of the species flowered during the months of March–April (28.04%), followed by August–September (26.31%). Abiotic factors have a significant influence on the distribution pattern and structure of climbers in the study area. The results show that the climbers react to the biotic environment in different ways. The findings will serve as the foundation for future botanical inventories and will be crucial for understanding the biological, ecological, and economic value of climbers in forest ecosystems. This will help forest management, conservation, and ecological restoration in the Himalayas.

The change in the local climate is attributed primarily to rapid urbanization, and this change has a strong influence on the adjacent areas. Lahore is one of the fast-growing metropolises in Pakistan, representing a swiftly urbanizing cluster. Anthropogenic materials sweep the usual land surfaces owing to the rapid urbanization, which adversely influences the environment causing the Surface Urban Heat Island (SUHI) effect. For the analysis of the SUHI effect, the parameter of utmost importance is the Land Surface Temperature (LST). The current research aimed to develop a model to forecast the LST to evaluate the SUHI effect on the surface of the Lahore district. For LST prediction, remote sensing data from Advanced Spaceborne Thermal Emission and the Reflection Radiometer Global Digital Elevation Model and Moderate-Resolution Imaging Spectroradiometer sensor are exploited. Different parameters are used to develop the Long Short-Term Memory (LSTM) model. In the present investigation, for the prediction of LST, the input parameters to the model included 10 years of LST data (2009 to 2019) and the Enhanced Vegetation Index (EVI), road density, and elevation. Data for the year 2020 are used to validate the outcomes of the LSTM model. An assessment of the measured and model-forecasted LST specified that the extent of mean absolute error is 0.27 K for both periods. In contrast, the mean absolute percentage error fluctuated from 0.12 to 0.14%. The functioning of the model is also assessed through the number of pixels of the research area, classified based on the error in the forecasting of LST. The LSTM model is contrasted with the Artificial Neural Network (ANN) model to evaluate the skill score factor of the LSTM model in relation to the ANN model. The skill scores computed for both periods expressed absolute values, which distinctly illustrated the efficiency of the LSTM model for better LST prediction compared to the ANN model. Thus, the LST prediction for evaluating the SUHI effect by the LSTM model is practically acceptable.