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61.
Zhang Xiliang Nguyen Hoang Bui Xuan-Nam Tran Quang-Hieu Nguyen Dinh-An Bui Dieu Tien Moayedi Hossein 《Natural Resources Research》2020,29(2):711-721
Natural Resources Research - Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of... 相似文献
62.
Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression 总被引：12，自引：2，他引：10
The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression
methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide
inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last
10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature.
Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map,
and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated
from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological
and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land
Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from
topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and
to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area
under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an
almost equal predicting capacity. 相似文献
63.
64.
Satellite Gravity Anomalies and Their Correlation with the Major Tectonic Features in the South China Sea 总被引：4，自引：0，他引：4
The South China Sea (SCS) is a region of interaction among three major plates: the Pacific, Indo-Australian and Eurasian. The collision of the Indian subcontinent with the Eurasian plate in the northwest, back-arc spreading at the center, and subduction beneath the Philippine plate along Manila trench in the east and the collision along Palawan trough in the south have produced complex tectonic features within and along the SCS. This investigation examines the satellite-derived gravity anomalies of the SCS and compares them with major tectonic features of the area. A map of Bouguer gravity anomaly is derived in conjunction with available seafloor topography to investigate the crustal structure. The residual isostatic gravity anomaly is calculated assuming that the Cenozoic sedimentary load is isostatically compensated. The features in the gravity anomalies in general correlate remarkably well with the major geological features, including offsets in the seafloor spreading segments, major faults, basins, seamounts and other manifestations of magmatism and volcanism on the seafloor. They also correlate with the presumed location of continental-oceanic crust boundary. The region underlain by oceanic crust in the central part of the SCS is characterized by a large positive Bouguer gravity anomaly (220–330 mgal) as well as large free-air and residual isostatic anomalies. There are, however, important differences among spreading segments. For example, in terms of free-air gravity anomaly, the southwest section of mid-ocean has an approximately 50 km wide belt of gravity low superimposed on a broad high of 45 mgal running NW–SE, whereas there are no similar features in other spreading segments. There are indications that gravity anomalies may represent lateral variation in upper crustal density structure. For instance, free air and isostatic anomalies show large positive anomalies in the east of the Namconson basin, which coincide with areas of dense volcanic material known from seismic surveys. The Red River Fault system are clearly identified in the satellite gravity anomalies, including three major faults, Songchay Fault in the southwest, Songlo Fault in the Northeast and Central Fault in the center of the basin. They are elongated in NW–SE direction between 20±30'N and 17°N and reach to Vietnam Scarp Fault around 16°30'N. It is also defined that the crustal density in the south side of the Central Basin is denser than that in the north side of the Central Basin. 相似文献
65.
Binh?Thai?PhamEmail author Dieu?Tien Bui Hamid?Reza?Pourghasemi Prakash?Indra M.?B.?Dholakia 《Theoretical and Applied Climatology》2017,128(1-2):255-273
The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning. 相似文献
66.
Binh Thai Pham Dieu Tien Bui Ha Viet Pham Hung Quoc Le Indra Prakash M. B. Dholakia 《Journal of the Indian Society of Remote Sensing》2017,45(4):673-683
Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management. 相似文献
67.
Dieu Tien Bui Tran Anh Tuan Harald Klempe Biswajeet Pradhan Inge Revhaug 《Landslides》2016,13(2):361-378
Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. 相似文献
68.
G. A. Vlasova M. N. Demenok Nguyen Ba Xuan Bui Hong Long 《Izvestiya Atmospheric and Oceanic Physics》2016,52(3):317-327
Based on numerical simulations, we calculate the integral water circulation of the South China Sea on the eastern Vietnam shelf in the Vietnam coastal current area. The main objective of simulations was to study the hydrodynamic structures of this current in the winter–summer interseasonal period. The calculations were performed for the period from April to June 1999, which had the necessary primary field data. Two types of atmospheric processes were considered: the first is characterized by a small pressure gradient over the South China Sea and the second includes tropical cyclones in the southern part of the sea. The simulation results showed that there are three hydrodynamic gyres in the study area during the given time period: two anticyclonic gyres and a cyclonic gyre that separates them, which together form a complex pattern of the Vietnam current. These gyres persist for the given types of atmospheric processes and are quasi-stationary structures. The Vietnam current carries coastal water masses from south to north within the anticyclonic gyres in summer and from north to south within the cyclonic gyres in winter. 相似文献
69.
Binh Thai Pham Dieu Tien Bui M. B. Dholakia Indra Prakash Ha Viet Pham 《Geotechnical and Geological Engineering》2016,34(6):1807-1824
The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards. 相似文献
70.
This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha?1 (average = 55.8 Mg ha?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha?1 (average = 81.47 Mg ha?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha?1 (average = 64.52 Mg C ha?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas. 相似文献