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.

Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is helpful to researchers and planners for better planning to control the loss of soil in the high severity zones. Plantation of trees and structures should be built like check dams, which effectively control the soil erosion process.

One of the key functions of global water resource management authorities is river water quality (WQ) COD assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques that are renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945 and PBIAS = -0.64, outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS).

Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are diminishing due to urban sprawl. In this study, we used a GIS-based DRASTIC model (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) to analyze the area’s hydrological attributes to assess the groundwater susceptibility to pollution. Considering the importance of anthropogenic activities, this research primarily utilizes an adjusted DRASTIC model called DRASTICA, which incorporates anthropogenic impact as a parameter in the model. The resulting map, which depicts vulnerability to groundwater contamination, reveals that 19% of the study area is classed as having high vulnerability, 42% has moderate vulnerability, 37% has low vulnerability, and 2% has very low vulnerability to groundwater contamination. The adopted validation process (nitrate parameter of water quality) revealed that the suggested DRASTICA model achieved better results than the established DRASTIC model in a built-up environment. We used the nitrate concentration in groundwater to verify the formulated results, and the single parameter sensitivity analysis and map removal sensitivity analysis to analyze the model sensitivity. The sensitivity analysis indicated that the groundwater vulnerability to pollution is largely influenced by anthropogenic impact and depth to the water table, thereby suggesting that anthropogenic impact must be explicitly tackled in such studies. The groundwater zones exposed to anthropogenic pollution can be better classified with the help of the proposed DRASTICA model, particularly in and around built-up environments. The responsible authorities can use this groundwater contamination data as an early warning sign, so they can take practical actions to avoid extra pressure on this vital resource.