Integrating multisource data and machine learning for supraglacial lake detection: Implications for environmental management and sustainable development goals in high mountainous regions.

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument (MSI) data, and surface slope, aspect, and elevation parameters from topographic data.

Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models.

Climatic and topographic controls on glacial changes (1973-2020) in Shigar Basin, Central Karakoram, Northern Pakistan.

The Hindukush, Karakorum, and Himalaya (HKH) mountains are often referred to as the "Third Pole" because of high snow, being a major freshwater resource and early indicator of climate change. Therefore, research on the dynamics of glacier changes and their relationship with climate and topographic variability is essential for sustainable water resource management and adaptation strategies in Pakistan. In this contribution, we delineated 187 glaciers and examined these glacier changes in the Shigar Basin from 1973 to 2020 using Corona, Landsat Operational Land Imager/Enhanced Thematic Mapper Plus/Thematic Mapper/Multispectral Scanner System (OLI/ETM/TM/MSS), Alaska Satellite Facility (ASF), and Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) imageries.

SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan.

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.

Assessment of glacier status and its controlling parameters from 1990 to 2018 of Hunza Basin, Western Karakorum.

Ice masses and snow of Hunza River Basin (HRB) are an important primary source of fresh water and lifeline for downstream inhabitants. Changing climatic conditions seriously put an impact on these available ice and snow masses. These glaciers may affect downstream population by glacial lake outburst floods (GLOF) and surge events due to climatic variation.

Expansion of Shishper Glacier lake and recent glacier lake outburst flood (GLOF), Gilgit-Baltistan, Pakistan.

Shishper lake is an ice-dammed lake in northern Pakistan that has drained twice within one (1) year. The parameters evaluated in this paper are the lake's area, volume, peak discharge, and its outburst events using various satellite images from November 2018 to June 2020. Based on satellite imagery and empirical approaches, the lake formed in November 2018 and reached a maximum of 0.

The role of sense of place, risk perception, and level of disaster preparedness in disaster vulnerable mountainous areas of Gilgit-Baltistan, Pakistan.

The study endeavored to analyze the risk perception, sense of place, and disaster preparedness in response to landslide disaster-prone mountain areas of Gilgit-Baltistan, Pakistan. To this end, we surveyed 315 rural residents of two vulnerable landslide districts (Hunza and Nagar) of Gilgit-Baltistan. To explore the relationships between the dimensions of risk perception, sense of place, and disaster preparedness, we used partial least squares (PLS) structural equation modeling (SEM) to test the hypotheses.

The dilemma of natural disasters: Impact on economy, fiscal position, and foreign direct investment alongside Belt and Road Initiative countries.

This paper investigates the damages and population affected by natural disasters based on percentile rankings, and analyzes the impact on the economy, per capita, fiscal balance, and foreign direct investment using novel panel algorithms including; Generalized Method of Moment (GMM), Crossectionally augmented Autoregressive Distributed Lags (CS-ARDL), and Driscoll & Kraay (DK) in Belt and Road initiative countries (B&RIC) over 1990-2018. The results indicate that severe natural disasters have negatively influenced economic growth with an average size of -0.016, which is transmitted to fiscal balance (-0.