Sandwaves on the Southeast Vietnam Shelf recorded by high resolution seismic profiles: formation and mechanism

The application of high resolution seismic data using boomer sound source has revealed a wide distribution of large-scale bedforms (sandwaves) on the Southeast Vietnam continental shelf. Bedforms that are a few meters high in wave height and hundreds of meters long in wavelength are primarily developed in the inner shelf (20–40 m) and considered to be formed under the present-day marine hydrodynamic conditions. Those bedforms developed in the deeper water (120 m) of the northernmost part of the continent can be interpreted as the relict morphological features formed during the latest sea-level lowstand of the late Pleistocene period. Two sediment transport paths have been identified on the basis of the bedform’s leeward orientation: northeast-southwest (along-shore) and north-south (cross-shore). A quantitative bottom current map is constructed from sandwave dimensions, surface sediments and measurement data. The strongest current velocities that gradually decrease toward the southwest are indicated by large sandwaves in the north (field B). Water depth, surficial sediment composition and bottom current are three factors that control the development of bedforms.     

The Nui Chua layered peridotite-gabbro complex as manifestation of Permo-Triassic mantle plume in northern Vietnam

New data on the age, composition, formation conditions, and ore-geochemical specialization of the Nui Chua layered peridotite-gabbro complex are reported. They evidence that the complex resulted from the Permo-Triassic mantle plume activity in northern Vietnam (southern framing of the Yangtze Platform). Two series of mafic and ultramafic rocks differing in ore productivity—layered (PGE-Cu-Ni) and pegmatoid (Fe-Ti-V)—have been recognized within the complex. The first estimates of the composition of their parental melts have been obtained.     

Land use/cover changes in the war-ravaged Jaffna Peninsula, Sri Lanka, 1984–early 2004

Temporal and spatial information on land use/cover is a prerequisite for effective planning decisions in the context of social and economic development. Satellite remote sensing data have become increasingly important in the study of land use/cover changes. This paper uses multitemporal satellite data to measure and spatially characterize land use/cover changes in the Jaffna Peninsula, northern Sri Lanka over the two decades from 1984 to early 2004 in terms of potential drivers. Over this period, the Jaffna Peninsula has been impacted severely by armed conflict between the government of Sri Lanka and the Liberation Tigers of Tamil Eelam. A postclassification method is used to compare land use/cover classes from satellite images using Geographical Information Systems (GIS) to analyze the spatial pattern of land use/cover changes over the study period. Results indicate that the land use/cover pattern has been very dynamic since early the 1980s, showing a remarkable decrease in agricultural land use and concomitant increase in non-agricultural land uses. The ethnic conflict and its consequences, particularly large-scale population migrations, have been the main driving forces for such land use/cover changes in the Jaffna Peninsula. The results of this study are not only important in aiding efforts to reconstruct this area after decades of physical and socioeconomic devastation, but should also prompt similarly urgent studies in other inaccessible war-torn areas of the world.     

Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees,Logistic Regression and J48 Decision Trees Methods: A Comparative Study

In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas.     

Geoheritage values in the Cat Ba islands, Vietnam

Geological wonders have been generally known as natural wonderful products. Resulted from geological processes, geological wonders are diverse in size that have geoheritage values that should be protected from damaging of substance, form and natural development. In a large scale, geological wonders can be geoheritage areas, containing several geodiversity elements that are geologically important or in a smaller scale, they can be geosites of heritage values (or geoheritage sites). In the delimitation of areas, having geoheritage values and the establishment of geoparks, the first thing is to recognise them as geosites and geoheritage areas that indicate great geological values. Besides the Ha Long bay, the world natural heritage with its outstanding aesthetic and geological values, the Cat Ba islands are typical and grandeur karst landscapes formed in tropical condition. Based on the geodiversity elements with their own geoheritage values on aesthetics, uniqueness and grandeur in the Cat Ba islands, the authors have recognised three geoheritage areas: the south cape of the Cat Ba embayment, Tung Gau (shelter), and the Lan Ha bay. Sites where Brachiopods, Crinoids and Tetracorals are exposed on the way through the island are considered as palaeontological geosites. The folds of limestone layers in the northern part of Cat Co 3 beach, with typical turbidite structures in carbonate formations are considered as a lithological geosite. The Devonian-Carboniferous boundary near the Cat Co 3 beach is regarded as a stratigraphical geosite while Que Kem and Turtle islands, etc. are considered as geomorphological geosites.     

Multi-hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi-hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi-hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi-hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi-hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi-hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi-hazard maps can provide a valuable point of reference for decision makers in sustainable land-use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.     

A novel computational approach for large deformation and post‐failure analyses of segmental retaining wall systems

Segmental retaining wall (SRW) systems are commonly used in geotechnical practice to stabilize cut and fill slopes. Because of their flexibility, these systems can tolerate minor movements and settlements without incurring damage or crack. Despite these advantages, very few numerical studies of large deformations and post‐failure behavior of SRW systems are found in the current literature. Traditional numerical methods, such as the finite element method, suffer from mesh entanglement, thus are unable to simulate large deformations and flexible behavior of retaining wall blocks in SRW systems. To overcome the above limitations, a novel computational framework based on the smoothed particle hydrodynamics (SPH) method was developed to simulate large deformations and post‐failure behavior of soils and retaining wall blocks in SRW systems. The proposed numerical framework is a hybrid continuum/discontinuum approach that can model soil as an elasto‐plastic material and retaining wall blocks as independent rigid bodies associated with both translational and rotational degrees of freedom. A new contact model is proposed within the SPH framework to simulate the interaction between the soil and the blocks and between the blocks. As an application of the proposed numerical method, a two‐dimensional simulation of an SRW collapse was simulated and compared to experimental results conducted under the same conditions. The results showed that the proposed computational approach provided satisfactory agreement with the experiment. This suggests that the new framework is a promising numerical approach to model SRW systems. Copyright © 2014 John Wiley & Sons, Ltd.     

Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network

Natural Resources Research - Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the...     

Estimation of Blast-Induced Air Overpressure in Quarry Mines Using Cubist-Based Genetic Algorithm

In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.