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Geological settings of the Karakoram Highway (KKH) increase the risk of natural disasters, threatening its regular operations. Predicting landslides along the KKH is challenging due to limitations in techniques, a challenging environment, and data availability issues. This study uses machine learning (ML) models and a landslide inventory to evaluate the relationship between landslide events and their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), and K Nearest Neighbor (KNN) models were used. A total of 303 landslide points were used to create an inventory, with 70% for training and 30% for testing. Susceptibility mapping used Fourteen landslide causative factors. The area under the curve (AUC) of a receiver operating characteristic (ROC) is employed to compare the accuracy of the models. The deformation of generated models in susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. The sensitive regions of the models showed elevated line-of-sight (LOS) deformation velocity. The XGBoost technique produces a superior Landslide Susceptibility map (LSM) for the region with the integration of SBAS-InSAR findings. This improved LSM offers predictive modeling for disaster mitigation and gives a theoretical direction for the regular management of KKH. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, the degree of human impact on land use is increasing. land use land cover change (LULC) is acknowledged as crucial factor in environmental change. The best way to understand historical land use patterns, changes, drivers, and developments is through a rigorous assessment of LULC changes. In this study, we aim to identify LULC changes from 2015 to 2022, and predict changes for 2030. Sentinel-2 images were employed to analyze LULC change patterns and predict future trends. The Random Forest algorithm was used to classify the various LULC classes with high accuracy and reliability. Multilayer Perceptron and Markov Chain Analysis (MLP-MCA) based Hybrid-Approach was employed to predict the future dynamics of LULC change for 2030. The study revealed that built-up area expanded 90.64 km2 from 2015 to 2022 due to natural resource substitution. Predictions indicate that 58.84% of the study area will be transform into built-up by 2030. [ABSTRACT FROM AUTHOR]

Gilgit-Baltistan, Pakistan, is particularly susceptible to landslides due to various geological, tectonics, meteorological, and anthropogenic factors consequently. However, the persisting conundrum of landslide database/data imbalance stands as a formidable challenge within this domain. To better stabilize the objective of landslide prediction, stacking ensemble Machine Learning and Generative Adversarial Network (GAN) were applied, because previous research in this area has mostly been limited by a lack of data. GAN is employed to synthesize training samples, ensuring the creation of a balanced dataset. Stacking ensemble architecture involves two stages of learning: the first class of learners incorporates diverse machine learning algorithms, while, the second level logistic regression model integrates prediction based on the strong learner, thereby enhancing overall prediction performance. To investigate landslide susceptibility in District Chilas, Northern Pakistan, we employed optical remote sensing and introduced a GAN with a Multi-Layers Hybrid Model (MLHM). This study involved the preparation of a spatial database with a total of 106 landslides and ten major landslide factors. We utilized a hybrid ensemble model and compared its performance with different algorithms like Conventional Neural Network, Artificial Neural network, Decision Tree, K-Nearest Neighbouring, and Hybrid Model, achieving accuracies of 0.91, 0.92, 0.90, 0.89, and 0.93, respectively. this approach has with Hybrid architecture learning accuracy of 0.98. The GAN with MLHM developed improved landslide susceptibility assessment with cross-comparison of Persistent Scattered Interferometric Synthetic Aperture Radar (PS-InSAR) investigation to ensure the safe functioning of KKH. [ABSTRACT FROM AUTHOR]