Landslide susceptibility assessment at the Wuning area,China: a comparison between multi-criteria decision making,bivariate statistical and machine learning methods

Natural Hazards - The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of...     

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.     

Long-term thermo-mechanical behavior of energy pile in dry sand

A small-scale pile has been developed in the laboratory to investigate the thermo-mechanical behavior of energy piles subjected to a significant number of thermal cycles. The pile (20 mm external diameter), installed in dry sand, was initially loaded at its head to 0, 20, 40 and 60% of its ultimate bearing capacity (500 N). At the end of each loading step, 30 heating/cooling cycles were applied. The long-term behavior of the pile was observed in terms of head settlement, axial force profile, soil and pile temperature, and stress in soil. The results evidence the irreversible settlement of the pile head induced by thermal cycles under constant load head. In addition, the incremental irreversible settlement that accumulates after each thermal cycle decreases when the number of cycles increases. The evolution of irreversible pile head settlement versus number of cycles can be reasonably predicted by an asymptotic equation.     

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.     

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.     

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. Th...     

Characteristics of Karst Ecosystems of Vietnam and Their Vulnerability to Human Impact

Karst in Vietnam covers an area of about 60,000 km2, i.e. 18 % of the surface of the country. The country has an annual average temperature of 24@, an annual average rainfall of 2300 mm and a relative humidity of about 90%. Karst in Vietnam is typified by peak cluster-depression landscapes ranging in elevation from 200 to over 2000 m. Tower and coastal karst landscapes also exit. Because of naturally favourable conditions, karst ecosystems are diverse and very rich. Higher plants (cormophytes) are abundant. They are represented by approximately 2000 species, 908 genera, 224 families, 86 orders and 7 phyla. They form a thick vegetation cover of evergreen tropical rainforest. Knowledge about lower plants is limited. The fauna is rich and diverse. Phyla such as Protozoa, Vermes, Mollusca and Arthropoda are yet ill known. Preliminary results show that the phylum Chordata is represented by 541 species from 80 families, 40 orders and 5 classes. There exist many precious and rare mammals, in particular some en     

Constructing a 3D structural block diagram of the Central Basin in Marmara Sea by means of bathymetric and seismic data

In this study we made a comparative interpretation of multibeam bathymetric and seismic reflection data with different resolutions and penetration properties collected in the Central Basin of the Marmara Sea. Our main objectives were (i) to investigate and compare the active tectonic deformation observed on the sea bottom and within the uppermost sedimentary layers to that of the deep-seated deformation within the limits of resolution and penetration of the available geophysical data and (ii) to build a three-dimensional (3D) block diagram of the active tectonic and buried features by means of a sliced mapping technique. In this approach, we produced slice maps of the active and buried structural features at selected depths and then combined them to form a 3D structural block diagram. Motivation for our work was to produce a 3D structural diagram to derive a more detailed image of the structural features in the Central Basin where there is no available 3D seismic data. The observations from the bathymetry and seismic data and developed 3D diagram support the presence of a through-going strike-slip fault that forms a rotational depression zone against a right-stepping strike-slip faulting causing a pull-apart basin in the Central Depression zone.