Features of the Distribution of Abnormal Gasgeochemical Fields in the Red River Rift (Gulf of Tonkin,South China Sea)

Doklady Earth Sciences - This paper reports the results of the third Russian–Vietnamese expedition (V.I. Il'ichev Pacific Oceanological Institute, Far East Branch, Russian Academy of...     

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.     

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.     

Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R²) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R². Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R² compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

     

Computational Intelligence Model for Estimating Intensity of Blast-Induced Ground Vibration in a Mine Based on Imperialist Competitive and Extreme Gradient Boosting Algorithms

Natural Resources Research - In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced ground vibration in a mine based on extreme gradient boosting (XGBoost)...     

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...     

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...