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Anthropogenic and natural ecosystems in coastal dunes provide considerable benefits to human well-being. However, to date, we still lack a good understanding of how ecosystem services (ES) supply varies from young dunes (e.g., embryo and fore dunes) to mature dunes (e.g., brown and red dunes). This study proposed a novel modelling methodology by integrating an expert-based matrix, a Bayesian Belief Network (BBN), a structural equation model, and a scenario development method. It aims at evaluating dune ecosystem services for the sustainable development of coastal areas. The model was tested using data collected from dunes in Vietnam. An expert-based matrix to assess the supply capacity of 18 ES in different types of dunes was generated with the participation of 21 interdisciplinary scientists. It was found that red dune ecosystems could supply the most regulation and cultural ecosystem services, while gray dunes provided the least amount. Results from a scenario analysis recommended that decision-making is able to optimize multiple ES by: (i) keeping embryo/fore dunes in their natural state instead of using them for mineral mining and urbanization; (ii) enlarging certified and protected forests areas in gray and yellow dunes; and (iii) optimizing cultural ES supply in red dunes.
Kinh Bac Dang; Thu Thuy Nguyen; Huu Hao Ngo; Benjamin Burkhard; Felix Müller; Van Bao Dang; Hieu Nguyen; Van Liem Ngo; Thi Phuong Nga Pham. Integrated methods and scenarios for assessment of sand dunes ecosystem services. Journal of Environmental Management 2021, 289, 112485 .
AMA StyleKinh Bac Dang, Thu Thuy Nguyen, Huu Hao Ngo, Benjamin Burkhard, Felix Müller, Van Bao Dang, Hieu Nguyen, Van Liem Ngo, Thi Phuong Nga Pham. Integrated methods and scenarios for assessment of sand dunes ecosystem services. Journal of Environmental Management. 2021; 289 ():112485.
Chicago/Turabian StyleKinh Bac Dang; Thu Thuy Nguyen; Huu Hao Ngo; Benjamin Burkhard; Felix Müller; Van Bao Dang; Hieu Nguyen; Van Liem Ngo; Thi Phuong Nga Pham. 2021. "Integrated methods and scenarios for assessment of sand dunes ecosystem services." Journal of Environmental Management 289, no. : 112485.
The Sin Quyen iron oxide–copper–gold (IOCG) deposit is located in the southwestern part of the Sin Quyen Formation, close to the Red River shear zone. The deposit is controlled by the Sin Quyen fault, parallel to the Red River fault system in North Vietnam. U-Pb dating of zircon and uraninite and 39Ar/40Ar dating of biotite and K-feldspar in the Sin Quyen deposit showed that the mineralization was emplaced in two main phases: the first phase formed magnetite, uraninite, and allanite of Precambrian age, between 520 and 744 Ma; the second most important phase introduced Cu-sulfides with Au, this event formed chalcopyrite, pyrrhotite, and pyrite in the temperature range 320 ± 40 °C between 88 and 22 Ma. In this study, a tectonic model was presented to explain when and how the Sin Quyen IOCG deposit and some other Cu-Au deposits along the Ailao Shan Red River Shear zone occurred in Cenozoic time.
Van-Hao Duong; Phan Trong Trinh; Thanh-Duong Nguyen; Adam Piestrzyski; Dinh Chau Nguyen; Jadwiga Pieczonka; Xuan Dac Ngo; Phong Tran Van; Binh Thai Pham; Huong Nguyen-Van; Liem Ngo Van; Dieu Tien Bui; Dang Vu Khac; Chi Tien Bui. Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone. Ore Geology Reviews 2021, 132, 104065 .
AMA StyleVan-Hao Duong, Phan Trong Trinh, Thanh-Duong Nguyen, Adam Piestrzyski, Dinh Chau Nguyen, Jadwiga Pieczonka, Xuan Dac Ngo, Phong Tran Van, Binh Thai Pham, Huong Nguyen-Van, Liem Ngo Van, Dieu Tien Bui, Dang Vu Khac, Chi Tien Bui. Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone. Ore Geology Reviews. 2021; 132 ():104065.
Chicago/Turabian StyleVan-Hao Duong; Phan Trong Trinh; Thanh-Duong Nguyen; Adam Piestrzyski; Dinh Chau Nguyen; Jadwiga Pieczonka; Xuan Dac Ngo; Phong Tran Van; Binh Thai Pham; Huong Nguyen-Van; Liem Ngo Van; Dieu Tien Bui; Dang Vu Khac; Chi Tien Bui. 2021. "Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone." Ore Geology Reviews 132, no. : 104065.
The increase of coastal erosion due to intense tropical storms and unsustainable urban development in Vietnam demands vulnerability assessments at different research scales. This study proposes (1) a new approach to classify coastlines and (2) suitable criteria to evaluate coastal vulnerability index (CVI) at national and regional/local scales. At the national scale, the Vietnamese coastline was separated into 72 cells from 8 coast types based on natural features, whereas the Center region of Vietnam was separated into 495 cells from 41 coast types based on both natural and socio-economic features. The assessments were carried out by using 17 criteria related to local land use/cover, socio-economic, and natural datasets. Some simplified variables for CVI calculation at the national scale were replaced by quantitative variables at regional/local scales, particularly geomorphology and socio-economic variables. As a result, more than 20% of Vietnam’s coastline has high CVI values, significantly more than 350 km of the coasts in the center part. The coastal landscapes with residential and tourism lands close to the beaches without protection forests have been strongly affected by storms’ erosion. The new approach is cost-effective in data use and processing and is ideal for identifying and evaluating the CVI index at different scales.
Cao Nguyen; Kinh Dang; Van Ngo; Van Dang; Quang Truong; Dang Nguyen; Tuan Giang; Thi Pham; Chi Ngo; Thi Hoang; Thi Dang. New Approach to Assess Multi-Scale Coastal Landscape Vulnerability to Erosion in Tropical Storms in Vietnam. Sustainability 2021, 13, 1004 .
AMA StyleCao Nguyen, Kinh Dang, Van Ngo, Van Dang, Quang Truong, Dang Nguyen, Tuan Giang, Thi Pham, Chi Ngo, Thi Hoang, Thi Dang. New Approach to Assess Multi-Scale Coastal Landscape Vulnerability to Erosion in Tropical Storms in Vietnam. Sustainability. 2021; 13 (2):1004.
Chicago/Turabian StyleCao Nguyen; Kinh Dang; Van Ngo; Van Dang; Quang Truong; Dang Nguyen; Tuan Giang; Thi Pham; Chi Ngo; Thi Hoang; Thi Dang. 2021. "New Approach to Assess Multi-Scale Coastal Landscape Vulnerability to Erosion in Tropical Storms in Vietnam." Sustainability 13, no. 2: 1004.
The main objective of the current study was to introduce a Deep Learning Neural Network (DLNN) model in landslide susceptibility assessments and compare its predictive performance with state-of-the-art machine learning models. The efficiency of the DLNN model was estimated for the Kon Tum Province, Viet Nam, an area characterized by the presence of landslide phenomena. Nine landslide related variables, elevation, slope angle, aspect, land use, normalized difference vegetation index, soil type, distance to faults, distance to geology boundaries, lithology cover, and 1,657 landslide locations, were used so as to produce the training and validation datasets during the landslide susceptibility assessment. The Frequency Ratio method was used so as to estimate the existing relation between the landslide-related variables and the presence of landslides, assigning to each variable class a weight value. Based on the results concerning the predictive ability of the landslide related variables which was evaluated using the Information ration method, all variables were further processed since they appear as highly predictive. The learning ability of the DLNN model has been evaluated and compared with a Multi Layer Preceptron Neural Network, a Support Vector Machine, a C4.5-Decision Tree model and a Random Forest model using the training dataset, whereas the predictive performance of each model has been evaluated and compared using the validation datasets. In order to evaluate their learning and predictive capacity of each model the classification accuracy, the sensitivity, the specificity and the area under the success and predictive rate curves (AUC) were calculated. Results showed that the proposed DLNN model had a higher performance than the four benchmark models. Although DLNN has been used seldom in landslide susceptibility assessments, the study highlights that the usage of deep learning approach could be considered as a satisfactory alternative approach for landslide susceptibility mapping.
Dieu Tien Bui; Paraskevas Tsangaratos; Viet-Tien Nguyen; Ngo Van Liem; Phan Trong Trinh. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA 2020, 188, 104426 .
AMA StyleDieu Tien Bui, Paraskevas Tsangaratos, Viet-Tien Nguyen, Ngo Van Liem, Phan Trong Trinh. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA. 2020; 188 ():104426.
Chicago/Turabian StyleDieu Tien Bui; Paraskevas Tsangaratos; Viet-Tien Nguyen; Ngo Van Liem; Phan Trong Trinh. 2020. "Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment." CATENA 188, no. : 104426.
Although coastal classification has been attended in recent years, it is still a complicated problem in quantitative geomorphological and hydrological sciences. Nowadays, the integration of deep learning in remote sensing and GIS analysis can quickly classify and detect different characteristics on both land and sea. Therefore, the authors proposed the use of a convolutional neural network (ConvNet) for coastal classification based on these technologies and geomorphic profile graphs. The primary input data is digital elevation/depth models obtained from ALOS and NOAA satellite. Eight hundred coastal samples representing eight types of coasts taken along the coastline in Vietnam were used for training and testing various ConvNets. As a result, three ConvNet models using three different optimizer functions were developed with the accuracies of about 98% and low values of the loss function. These models were used to classify 1029 in 1150 coasts (equal to 89%) in Vietnam. Nearly 11% of Vietnamese coasts could not be defined by three ConvNet models due to their complex geomorphic profile graphs, and require assessments of other natural components. The trained ConvNet models can potentially update new coastal types in different tropical countries towards coastal classification on national and global scales.
Kinh Bac Dang; Van Bao Dang; Quang Thanh Bui; Van Vuong Nguyen; Thi Phuong Nga Pham; Van Liem Ngo. A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data. IEEE Access 2020, 8, 11824 -11839.
AMA StyleKinh Bac Dang, Van Bao Dang, Quang Thanh Bui, Van Vuong Nguyen, Thi Phuong Nga Pham, Van Liem Ngo. A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data. IEEE Access. 2020; 8 (99):11824-11839.
Chicago/Turabian StyleKinh Bac Dang; Van Bao Dang; Quang Thanh Bui; Van Vuong Nguyen; Thi Phuong Nga Pham; Van Liem Ngo. 2020. "A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data." IEEE Access 8, no. 99: 11824-11839.
Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.
Viet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability 2019, 11, 7118 .
AMA StyleViet-Tien Nguyen, Trong Hien Tran, Ngoc Anh Ha, Van Liem Ngo, Al-Ansari Nadhir, Van Phong Tran, Huu Duy Nguyen, Malek M. A., Ata Amini, Indra Prakash, L.S. Ho, Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability. 2019; 11 (24):7118.
Chicago/Turabian StyleViet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam." Sustainability 11, no. 24: 7118.
We studied recent tectonics and present-day geodynamics in the Ninh Thuan nuclear power plants and surrounding regions to reveal seismogenic faults, deformation and evaluation of seismotectonics using various methods such as remote sensing, GPS, seismic interpretation, and stress and strain analysis. We based our study on geomorphological investigation, satellite images, fault gouge, drilling core, fault scarps and analysis of offshore seismic profiles to determine the capable faults in the studied region. Using international reference frame ITRF08, we determined the absolute velocities of the GPS stations with a slip rate to the east of 22.5 to 25.3 mm/year and to the south of 4.4 to 8.4 mm/year. The present strain rate was determined from present tectonic velocities that were consistent with the recent strain rates determined from topographic profiles, the slip rate of capable faults, and the thickness of Pleistocene sediments. The present strain rate variation from 10 to 30 nano per year demonstrated that the studied region has been weakly deformed under a stable tectonic regime. The state of stress determined from the fault population, focal mechanism, borehole breakouts and drilling-induced tensile fracture methods indicated that the area was deformed primarily under a strike-slip regime with a small extensive component. The maximum credible earthquake was determined from the dimensions of the capable fault and state of stress using various methods. From these capable faults, we suggest monitoring the faults, which could produce a Maximum Credible Earthquake (MCE) over a range of 5.9–6.5.
Huong Nguyen-Van; Tran Van Phong; Phan Trong Trinh; Ngo Van Liem; Bui Nhi Thanh; Binh Thai Pham; Dieu Tien Bui; Nguyen Bieu; Hoang Quang Vinh; Nguyen Quang Xuyen; Nguyen Dang Tuc; Bui Van Thom; Nguyen Viet Thuan; Bui Thi Thao; Lai Hop Phong; Vu Duy Vinh; Mai Thanh Tan; Vy Quoc Hai; Nguyen Mai Lan; Tran Quoc Cuong; Pham Thi Thu Hang; Vu Van Ha; Cu Minh Hoang; Duong Van Hao; Tong Phuoc Hoang Son. Recent tectonics, geodynamics and seismotectonics in the Ninh Thuan Nuclear Power plants and surrounding regions, South Vietnam. Journal of Asian Earth Sciences 2019, 187, 104080 .
AMA StyleHuong Nguyen-Van, Tran Van Phong, Phan Trong Trinh, Ngo Van Liem, Bui Nhi Thanh, Binh Thai Pham, Dieu Tien Bui, Nguyen Bieu, Hoang Quang Vinh, Nguyen Quang Xuyen, Nguyen Dang Tuc, Bui Van Thom, Nguyen Viet Thuan, Bui Thi Thao, Lai Hop Phong, Vu Duy Vinh, Mai Thanh Tan, Vy Quoc Hai, Nguyen Mai Lan, Tran Quoc Cuong, Pham Thi Thu Hang, Vu Van Ha, Cu Minh Hoang, Duong Van Hao, Tong Phuoc Hoang Son. Recent tectonics, geodynamics and seismotectonics in the Ninh Thuan Nuclear Power plants and surrounding regions, South Vietnam. Journal of Asian Earth Sciences. 2019; 187 ():104080.
Chicago/Turabian StyleHuong Nguyen-Van; Tran Van Phong; Phan Trong Trinh; Ngo Van Liem; Bui Nhi Thanh; Binh Thai Pham; Dieu Tien Bui; Nguyen Bieu; Hoang Quang Vinh; Nguyen Quang Xuyen; Nguyen Dang Tuc; Bui Van Thom; Nguyen Viet Thuan; Bui Thi Thao; Lai Hop Phong; Vu Duy Vinh; Mai Thanh Tan; Vy Quoc Hai; Nguyen Mai Lan; Tran Quoc Cuong; Pham Thi Thu Hang; Vu Van Ha; Cu Minh Hoang; Duong Van Hao; Tong Phuoc Hoang Son. 2019. "Recent tectonics, geodynamics and seismotectonics in the Ninh Thuan Nuclear Power plants and surrounding regions, South Vietnam." Journal of Asian Earth Sciences 187, no. : 104080.