RESUMO
Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements.
RESUMO
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.