Pub Date : 2023-06-07DOI: 10.15625/2615-9783/18408
Dien Tran Ngoc, Ha Vu Van, Amelie Paszkowski, Quang Nguyen Minh, Dao Ngo Thi, Min Nguyen Thi, Tung Dang Xuan, Dung Nguyen Chi, Tuyen Vu Ngoc, Tung Nguyen Khanh, Huan Tran Ngoc
The Ba Lat estuary is part of the downstream stretch of the Red River Delta, the second-largest Delta in Vietnam and the 12th largest in the world. This study analyses GAT borehole sediments in the Ba Lat estuary area to assess environmental changes during the Late Pleistocene - Holocene period. This entailed detailed analyses of 70 m-deep borehole data from the Ba Lat estuary area, including structural analysis of 70 m-deep sediment core samples, 230 samples of grain size, 49 samples of Foraminifera, five samples of petrographic thin slices, and four samples of radiocarbon dating. The data reveal nine sedimentary facies, including river channel sand facies, floodplain clayey silt facies, tidal flat sandy-silty facies, bay clayey silt facies, pro-delta clayey silt facies, delta front sandy-silty clay facies, mouth bar sand facies, tidal flat sandy - silty clay facies, and delta plain silty clay facies. The combined nine sedimentary facies formed sequentially in time, representing the evolution of the sedimentary environment from the Late Pleistocene to the Holocene and the evolutionary process from the continental to the estuarine and Delta environment. The results also enable the geographic identification and delineation of the incised valley in the Red River Delta during the Late Pleistocene to Holocene period. The sedimentation rate in the incised valley varies from period to period. In the sedimentation phase of the incised valley, the average accretion rate reached 11.64 mm/year. In contrast, during the open sea regime (shallow sea near the coast), the accretion rate was observed to be very low, with a rate of 1.27 mm/year and the period of delta formation had the highest accretion rate, reaching 13.41 mm/year.
{"title":"The late Pleistocene - Holocene sedimentary evolution in the Ba Lat River mouth area of the Red River Delta","authors":"Dien Tran Ngoc, Ha Vu Van, Amelie Paszkowski, Quang Nguyen Minh, Dao Ngo Thi, Min Nguyen Thi, Tung Dang Xuan, Dung Nguyen Chi, Tuyen Vu Ngoc, Tung Nguyen Khanh, Huan Tran Ngoc","doi":"10.15625/2615-9783/18408","DOIUrl":"https://doi.org/10.15625/2615-9783/18408","url":null,"abstract":"The Ba Lat estuary is part of the downstream stretch of the Red River Delta, the second-largest Delta in Vietnam and the 12th largest in the world. This study analyses GAT borehole sediments in the Ba Lat estuary area to assess environmental changes during the Late Pleistocene - Holocene period. This entailed detailed analyses of 70 m-deep borehole data from the Ba Lat estuary area, including structural analysis of 70 m-deep sediment core samples, 230 samples of grain size, 49 samples of Foraminifera, five samples of petrographic thin slices, and four samples of radiocarbon dating. \u0000The data reveal nine sedimentary facies, including river channel sand facies, floodplain clayey silt facies, tidal flat sandy-silty facies, bay clayey silt facies, pro-delta clayey silt facies, delta front sandy-silty clay facies, mouth bar sand facies, tidal flat sandy - silty clay facies, and delta plain silty clay facies. The combined nine sedimentary facies formed sequentially in time, representing the evolution of the sedimentary environment from the Late Pleistocene to the Holocene and the evolutionary process from the continental to the estuarine and Delta environment. \u0000The results also enable the geographic identification and delineation of the incised valley in the Red River Delta during the Late Pleistocene to Holocene period. The sedimentation rate in the incised valley varies from period to period. In the sedimentation phase of the incised valley, the average accretion rate reached 11.64 mm/year. In contrast, during the open sea regime (shallow sea near the coast), the accretion rate was observed to be very low, with a rate of 1.27 mm/year and the period of delta formation had the highest accretion rate, reaching 13.41 mm/year.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-07DOI: 10.15625/2615-9783/18407
Tuan Giang Linh, Bac Dang Kinh, Quang Bui Thanh
Changes to the coastline or shoreline arise from the water's dynamic interaction with the land surface, which is triggered by ocean currents, waves, and winds. Various methods have been proposed to identify and monitor coastlines and shorelines, but their outcomes are uncertain. This study proposes indicators for identifying coastlines and shorelines in the fields and on the remote sensing data. Different pixel- and object-based machine learning (ML) models were built to automatically interpret coastlines and shorelines from high-resolution remote sensing images and monitor coastal erosion in Vietnam. Two pixel-based models using Random Forest and SVM structures and eight object-based models using U-Net, and U-Net3+ structures were trained. All models were trained using the high-resolution images gathered using Google Earth Pro as input data. The U-Net achieves the most remarkable performance of 98% with a loss function of 0.16 when utilizing an input-image size of 512×512. Object-based models have shown higher performance in analyzing coastlines and shorelines with linear and continuous structures than pixel-based models. Additionally, the coastline is appropriate to evaluate coastal erosion induced by the effect of sea-level rise during storms. At the same time, the shoreline is suited to observe seasonal tidal fluctuations or the instantaneous movements of current waves. Under the pressure of tourist development, the coasts in Danang and Quang Nam provinces have been eroded in the last 10 years. River and ocean currents also cause erosion in the southern Cua Dai estuary. In the future, the trained U-Net model can be used to monitor the changes in coastlines and shorelines worldwide.
{"title":"Coastline and shoreline change assessment in sandy coasts based on machine learning models and high-resolution satellite images","authors":"Tuan Giang Linh, Bac Dang Kinh, Quang Bui Thanh","doi":"10.15625/2615-9783/18407","DOIUrl":"https://doi.org/10.15625/2615-9783/18407","url":null,"abstract":"Changes to the coastline or shoreline arise from the water's dynamic interaction with the land surface, which is triggered by ocean currents, waves, and winds. Various methods have been proposed to identify and monitor coastlines and shorelines, but their outcomes are uncertain. This study proposes indicators for identifying coastlines and shorelines in the fields and on the remote sensing data. Different pixel- and object-based machine learning (ML) models were built to automatically interpret coastlines and shorelines from high-resolution remote sensing images and monitor coastal erosion in Vietnam. Two pixel-based models using Random Forest and SVM structures and eight object-based models using U-Net, and U-Net3+ structures were trained. All models were trained using the high-resolution images gathered using Google Earth Pro as input data. The U-Net achieves the most remarkable performance of 98% with a loss function of 0.16 when utilizing an input-image size of 512×512. \u0000Object-based models have shown higher performance in analyzing coastlines and shorelines with linear and continuous structures than pixel-based models. Additionally, the coastline is appropriate to evaluate coastal erosion induced by the effect of sea-level rise during storms. At the same time, the shoreline is suited to observe seasonal tidal fluctuations or the instantaneous movements of current waves. Under the pressure of tourist development, the coasts in Danang and Quang Nam provinces have been eroded in the last 10 years. River and ocean currents also cause erosion in the southern Cua Dai estuary. In the future, the trained U-Net model can be used to monitor the changes in coastlines and shorelines worldwide.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49558751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.15625/2615-9783/18348
Viengthong Xayavong, Minh Vu Duc, Sounthone Singsoupho, Duong Nguyen Anh, Prasad K.N.D., Tuan Vu Minh, Chung Do Anh
2D Electrical Resistivity Imaging (2D-ERI) and Seismic Refraction Tomography (SRT) are non-destructive techniques widely used for numerous geophysical investigations, namely, structural geology mapping, archaeological, engineering, and groundwater investigations. The present study aims to define potential groundwater zones in the Thakhek district of Khammouane Province, Laos, by 2D-ERI and SRT methods; due to the limitation of groundwater potential information, monitoring and evaluation activities regarding groundwater quantity and quality have not been conducted in this study area. The 2D-ERI measurement is based on the Wenner configuration with an electrode spacing of 10-160 m. In contrast, SRT uses a 6.5 kg sledgehammer for a seismic source with a 4 m geophone interval. The results indicate moderate resistivity values ranging from 18.8-71 Ohm-m, and seismic velocities ranging from 1220-2140 m/s were found at 12-30 m in the study region, illustrating the existence of a groundwater/aquifer at this depth. These results correlate well with drilling results from the borehole measurements in the Thakhek district, where the water levels were found at a depth of 12 m for borehole 1 and 15 m for borehole 2. The present study also demonstrates the correlation between 2D-ERI and SRT techniques. The adopted methods favor groundwater identification in the study region and other areas with similar geology formations.
{"title":"Combination of 2D-Electrical Resistivity Imaging and Seismic Refraction Tomography methods for groundwater potential assessments: A case study of Khammouane province, Laos","authors":"Viengthong Xayavong, Minh Vu Duc, Sounthone Singsoupho, Duong Nguyen Anh, Prasad K.N.D., Tuan Vu Minh, Chung Do Anh","doi":"10.15625/2615-9783/18348","DOIUrl":"https://doi.org/10.15625/2615-9783/18348","url":null,"abstract":"2D Electrical Resistivity Imaging (2D-ERI) and Seismic Refraction Tomography (SRT) are non-destructive techniques widely used for numerous geophysical investigations, namely, structural geology mapping, archaeological, engineering, and groundwater investigations. The present study aims to define potential groundwater zones in the Thakhek district of Khammouane Province, Laos, by 2D-ERI and SRT methods; due to the limitation of groundwater potential information, monitoring and evaluation activities regarding groundwater quantity and quality have not been conducted in this study area. The 2D-ERI measurement is based on the Wenner configuration with an electrode spacing of 10-160 m. In contrast, SRT uses a 6.5 kg sledgehammer for a seismic source with a 4 m geophone interval. The results indicate moderate resistivity values ranging from 18.8-71 Ohm-m, and seismic velocities ranging from 1220-2140 m/s were found at 12-30 m in the study region, illustrating the existence of a groundwater/aquifer at this depth. These results correlate well with drilling results from the borehole measurements in the Thakhek district, where the water levels were found at a depth of 12 m for borehole 1 and 15 m for borehole 2. The present study also demonstrates the correlation between 2D-ERI and SRT techniques. The adopted methods favor groundwater identification in the study region and other areas with similar geology formations.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43820002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-15DOI: 10.15625/2615-9783/18338
Viet-Ha Nhu, Binh Thai Pham, Dieu Bui Tien
In geotechnical engineering, soil shear strength is one of the most important parameters used in the design and construction of construction projects. However, determining this parameter in the laboratory is costly and time-consuming. Therefore, the main objective of this work is to develop a new alternative machine learning approach based on extreme learning machine (ELM) and Particle Swarm Optimization (PSO), namely PSO-ELM, for the shear strength prediction of soil for the Hoa Vuong new urban project in Nam Dinh province, North Vietnam. For this purpose, twelve soil parameters were collected on data from a survey of 155 soil samples to construct and validate the proposed model. We assessed the model's performance using the root-mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2). We compared the model's capability with five benchmark models, support vector regression (SVR), Gaussian process (GP), multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN), and the fast-decision tree (Fast-DT). The results revealed that the proposed PSO-ELM model yielded the highest prediction performance and outperformed the five benchmark models. It suggests that PSO-ELM can be an alternative method in estimating the shear strength of soil that would help geotechnical engineers reduce the cost of construction.
{"title":"A novel swarm intelligence optimized extreme learning machine for predicting soil shear strength: A case study at Hoa Vuong new urban project (Vietnam)","authors":"Viet-Ha Nhu, Binh Thai Pham, Dieu Bui Tien","doi":"10.15625/2615-9783/18338","DOIUrl":"https://doi.org/10.15625/2615-9783/18338","url":null,"abstract":"In geotechnical engineering, soil shear strength is one of the most important parameters used in the design and construction of construction projects. However, determining this parameter in the laboratory is costly and time-consuming. Therefore, the main objective of this work is to develop a new alternative machine learning approach based on extreme learning machine (ELM) and Particle Swarm Optimization (PSO), namely PSO-ELM, for the shear strength prediction of soil for the Hoa Vuong new urban project in Nam Dinh province, North Vietnam. For this purpose, twelve soil parameters were collected on data from a survey of 155 soil samples to construct and validate the proposed model. We assessed the model's performance using the root-mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2). We compared the model's capability with five benchmark models, support vector regression (SVR), Gaussian process (GP), multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN), and the fast-decision tree (Fast-DT). The results revealed that the proposed PSO-ELM model yielded the highest prediction performance and outperformed the five benchmark models. It suggests that PSO-ELM can be an alternative method in estimating the shear strength of soil that would help geotechnical engineers reduce the cost of construction.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45062149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.15625/2615-9783/18303
Thao Nguyen Thien Phuong, Thai Nguyen Duy, Ha Nguyen Thi Thu, Vinh Pham Quang, Thanh Dinh Xuan
Water stored by reservoirs is critical for irrigation, electricity generation, drinking water supply, recreation, fisheries, and flood control. Therefore, the reservoir's water storage volume (SW) must be measured and monitored frequently for better watershed management. Since SW data is often not publicly available, finding a method to quantify SW objectively and accurately but to facilitate local water management is necessary. This study proposes a method for monitoring water surface area and storage volume using multi-sensor satellite remote sensing data through the Tuyen Quang Reservoir case study in Northern Vietnam. Accordingly, the water surface area was first delineated from multi-temporal optical satellite images, such as Landsat series and Sentinel-2 images, using the Modified Normalized Difference Water Index and resampled into 30-m pixel resolution data. Using the Shuttle Radar Topography Mission Digital Elevation Model data, the water depth at each pixel was then calculated by the difference between its elevation and the reservoir shoreline's mean elevation. The results showed that the reservoir's water surface area increased rapidly during 2003-2007 (from 579 ha to 5,516 ha), fluctuated insignificantly in 2008-2020, and reached 7,196 ha in 2021. Consequently, SW was raised from 11.8 million m3 in 2003 to 1.68 billion m3 in 2021. Our estimations agree with the depth and SW of Tuyen Quang Reservoir published in 2019. Our proposed method could be an effective water resource management tool in developing countries where the number of impounding reservoirs increases dramatically yearly without the financial afford to build gauging stations.
{"title":"Monitoring the storage volume of impounding reservoirs in inaccessible regions using multi-sensor satellite data: the case study of Tuyen Quang Reservoir (Vietnam)","authors":"Thao Nguyen Thien Phuong, Thai Nguyen Duy, Ha Nguyen Thi Thu, Vinh Pham Quang, Thanh Dinh Xuan","doi":"10.15625/2615-9783/18303","DOIUrl":"https://doi.org/10.15625/2615-9783/18303","url":null,"abstract":"Water stored by reservoirs is critical for irrigation, electricity generation, drinking water supply, recreation, fisheries, and flood control. Therefore, the reservoir's water storage volume (SW) must be measured and monitored frequently for better watershed management. Since SW data is often not publicly available, finding a method to quantify SW objectively and accurately but to facilitate local water management is necessary. This study proposes a method for monitoring water surface area and storage volume using multi-sensor satellite remote sensing data through the Tuyen Quang Reservoir case study in Northern Vietnam. Accordingly, the water surface area was first delineated from multi-temporal optical satellite images, such as Landsat series and Sentinel-2 images, using the Modified Normalized Difference Water Index and resampled into 30-m pixel resolution data. Using the Shuttle Radar Topography Mission Digital Elevation Model data, the water depth at each pixel was then calculated by the difference between its elevation and the reservoir shoreline's mean elevation. The results showed that the reservoir's water surface area increased rapidly during 2003-2007 (from 579 ha to 5,516 ha), fluctuated insignificantly in 2008-2020, and reached 7,196 ha in 2021. Consequently, SW was raised from 11.8 million m3 in 2003 to 1.68 billion m3 in 2021. Our estimations agree with the depth and SW of Tuyen Quang Reservoir published in 2019. Our proposed method could be an effective water resource management tool in developing countries where the number of impounding reservoirs increases dramatically yearly without the financial afford to build gauging stations. ","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49224654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.15625/2615-9783/18284
Thanh- Ngo Duc
This study examines the characteristics and trends of extreme rainfall in Northern Vietnam from 1961 to 2018, using daily rainfall data collected from 37 meteorological stations. The study reveals that the average annual rainfall varies significantly across stations, ranging from 1140 mm to 4758 mm. The rainy season accounts for 73% to 92% of the total annual rainfall. Most stations show a declining trend in the annual total rainfall during wet days (PRCPTOT) and the number of wet days (WDAY), while rainfall intensity (SDII) has increased in most stations, particularly during the dry season. This can be attributed to an increase in PRCPTOT and a decrease in WDAY in the dry season. The study also finds a general decreasing trend in the annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day), as well as for the number of moderate (R16mm) and heavy (R50mm) rainfall days. However, most stations in Northern Vietnam demonstrate no trend in the annual maximum number of consecutive dry days (CDD) and the annual maximum number of consecutive wet days (CWD). Furthermore, the frequency of extreme rainfall events in Northern Vietnam exceeding the 5-year and 10-year return values of 1961-2018 has decreased in recent decades at many stations. Overall, the findings of this study provide insights into the changing patterns of extreme rainfall in Northern Vietnam, with significant implications for climate change adaptation and disaster risk reduction efforts in the region.
{"title":"Rainfall extremes in Northern Vietnam: a comprehensive analysis of patterns and trends","authors":"Thanh- Ngo Duc","doi":"10.15625/2615-9783/18284","DOIUrl":"https://doi.org/10.15625/2615-9783/18284","url":null,"abstract":"This study examines the characteristics and trends of extreme rainfall in Northern Vietnam from 1961 to 2018, using daily rainfall data collected from 37 meteorological stations. The study reveals that the average annual rainfall varies significantly across stations, ranging from 1140 mm to 4758 mm. The rainy season accounts for 73% to 92% of the total annual rainfall. Most stations show a declining trend in the annual total rainfall during wet days (PRCPTOT) and the number of wet days (WDAY), while rainfall intensity (SDII) has increased in most stations, particularly during the dry season. This can be attributed to an increase in PRCPTOT and a decrease in WDAY in the dry season. The study also finds a general decreasing trend in the annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day), as well as for the number of moderate (R16mm) and heavy (R50mm) rainfall days. However, most stations in Northern Vietnam demonstrate no trend in the annual maximum number of consecutive dry days (CDD) and the annual maximum number of consecutive wet days (CWD). Furthermore, the frequency of extreme rainfall events in Northern Vietnam exceeding the 5-year and 10-year return values of 1961-2018 has decreased in recent decades at many stations. Overall, the findings of this study provide insights into the changing patterns of extreme rainfall in Northern Vietnam, with significant implications for climate change adaptation and disaster risk reduction efforts in the region.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43237814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-19DOI: 10.15625/2615-9783/18281
H. Nguyễn-Văn, I. Unkel, D. Nguyễn-Thùy, Thái Nguyễn-Đình, Đỗ Trọng Quốc, Đặng Xuân Tùng, Nguyen Thi hong, Đinh Xuân Thành, Nguyễn Thị Ánh Nguyệt, Nguyen Hong Quan, Đào Trung Hoàn, Nguyễn Thị Huyền Trang, Phạm Lê Tuyết Nhung, Lê Nguyệt Anh, Vũ Văn Hà, A. Ojala, A. Schimmelmann, P. Sauer
Global warming enhances atmospheric moisture loading and will likely affect monsoon strength in Vietnam. Without a long written history in Vietnam, we need to rely on geoarchives such as lake sediments to reconstruct past monsoon variability and regional paleoenvironmental fluctuations and evaluate current climatic trends. Natural lakes in the Central Highlands of Vietnam have the potential to have recorded shifts of the monsoon belt over glacial/interglacial cycles. Since 2016, the EOS Geoscience Research Group at Vietnam National University, Hanoi (EOS group) has collaborated with international partners to collect and analyze Vietnamese lake sediments to reconstruct the Pleistocene-Holocene climate history. Numerous sediment cores have been retrieved from Biển Hồ, Ia Bang, Ea Tyn, Ea Sno and Lak lakes between 2016 and 2022. Of special importance is a 25 m long sediment core from Biển Hồ Lake (Gia Lai province) covering the last 57 ka, which the team retrieved in April 2021. Sediment cores were analyzed for geochemistry, sedimentology, magnetic susceptibility, and radiocarbon dating. This paper reviews the status of our currently available sedimentary records to assess the paleoenvironmental potential of lacustrine sediments in the Central Highlands of Vietnam. Current data suggest that the lakes in the Central Highlands provide reliable sedimentological and geochemical records and bear the potential to reconstruct the paleoenvironmental and paleoclimatic conditions in Vietnam across several glacial periods, with a high-resolution record at least in the Holocene. The records contribute to quantifying the effects of monsoon variability and assessing the changes in hydrological conditions before and after the onset of human land use by comparing different lakes in the region. Future fieldwork will focus on retrieving longer lake sediment sequences from the Central Highlands, possibly covering the full interglacial-glacial cycle (i.e. the last 125 ka, back to MIS-5e), and on the assessment of comparable lake archives in other parts of Vietnam where the timing and character of monsoon-related climatic variations may have differed.
{"title":"Paleoenvironmental potential of lacustrine sediments in the Central Highlands of Vietnam: a review on the state of research","authors":"H. Nguyễn-Văn, I. Unkel, D. Nguyễn-Thùy, Thái Nguyễn-Đình, Đỗ Trọng Quốc, Đặng Xuân Tùng, Nguyen Thi hong, Đinh Xuân Thành, Nguyễn Thị Ánh Nguyệt, Nguyen Hong Quan, Đào Trung Hoàn, Nguyễn Thị Huyền Trang, Phạm Lê Tuyết Nhung, Lê Nguyệt Anh, Vũ Văn Hà, A. Ojala, A. Schimmelmann, P. Sauer","doi":"10.15625/2615-9783/18281","DOIUrl":"https://doi.org/10.15625/2615-9783/18281","url":null,"abstract":"Global warming enhances atmospheric moisture loading and will likely affect monsoon strength in Vietnam. Without a long written history in Vietnam, we need to rely on geoarchives such as lake sediments to reconstruct past monsoon variability and regional paleoenvironmental fluctuations and evaluate current climatic trends. Natural lakes in the Central Highlands of Vietnam have the potential to have recorded shifts of the monsoon belt over glacial/interglacial cycles. Since 2016, the EOS Geoscience Research Group at Vietnam National University, Hanoi (EOS group) has collaborated with international partners to collect and analyze Vietnamese lake sediments to reconstruct the Pleistocene-Holocene climate history. Numerous sediment cores have been retrieved from Biển Hồ, Ia Bang, Ea Tyn, Ea Sno and Lak lakes between 2016 and 2022. Of special importance is a 25 m long sediment core from Biển Hồ Lake (Gia Lai province) covering the last 57 ka, which the team retrieved in April 2021. Sediment cores were analyzed for geochemistry, sedimentology, magnetic susceptibility, and radiocarbon dating. This paper reviews the status of our currently available sedimentary records to assess the paleoenvironmental potential of lacustrine sediments in the Central Highlands of Vietnam. Current data suggest that the lakes in the Central Highlands provide reliable sedimentological and geochemical records and bear the potential to reconstruct the paleoenvironmental and paleoclimatic conditions in Vietnam across several glacial periods, with a high-resolution record at least in the Holocene. The records contribute to quantifying the effects of monsoon variability and assessing the changes in hydrological conditions before and after the onset of human land use by comparing different lakes in the region. Future fieldwork will focus on retrieving longer lake sediment sequences from the Central Highlands, possibly covering the full interglacial-glacial cycle (i.e. the last 125 ka, back to MIS-5e), and on the assessment of comparable lake archives in other parts of Vietnam where the timing and character of monsoon-related climatic variations may have differed.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47316583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-30DOI: 10.15625/2615-9783/18206
Luan Thanh Pham, K. Prasad
In this study, structural lineaments and fracture zones of the northern region of the Central Indian Ridge have been determined using gravity data from XGM2019e_2159 global gravity model. In this scope, firstly, the edge detection performances of the gradient amplitude of the tilt angle (THDR), theta map (TM), improved local phase (ILP), and improved logistic (IL) methods have been evaluated on synthetic examples. The results show that the IL method effectively avoids false edges and produces high-resolution edges. Then, the methods are applied to the gravity anomaly of the northern region of the Central Indian Ridge. It has been determined that the most prominent structural lineaments observed over the region are in the NE-SW and NW-SE directions. These trends match reasonably with the significant trends of the Tilt depth solutions that show a depth range of 2.2 km to 7 km for different geological structures. In addition, the obtained results are compatible with the known fracture zones of the study area. The findings help us to improve our understanding of the structure and tectonic framework of the study region.
{"title":"Analysis of gravity data for extracting structural features of the northern region of the Central Indian Ridge","authors":"Luan Thanh Pham, K. Prasad","doi":"10.15625/2615-9783/18206","DOIUrl":"https://doi.org/10.15625/2615-9783/18206","url":null,"abstract":"In this study, structural lineaments and fracture zones of the northern region of the Central Indian Ridge have been determined using gravity data from XGM2019e_2159 global gravity model. In this scope, firstly, the edge detection performances of the gradient amplitude of the tilt angle (THDR), theta map (TM), improved local phase (ILP), and improved logistic (IL) methods have been evaluated on synthetic examples. The results show that the IL method effectively avoids false edges and produces high-resolution edges. Then, the methods are applied to the gravity anomaly of the northern region of the Central Indian Ridge. It has been determined that the most prominent structural lineaments observed over the region are in the NE-SW and NW-SE directions. These trends match reasonably with the significant trends of the Tilt depth solutions that show a depth range of 2.2 km to 7 km for different geological structures. In addition, the obtained results are compatible with the known fracture zones of the study area. The findings help us to improve our understanding of the structure and tectonic framework of the study region.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47021980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-30DOI: 10.15625/2615-9783/18204
Duc Dao Minh, Minh Vu Cao, Yen Hoang Hai, Loc Nguyen The, Duc Do Minh
Rainfall infiltration on the slope increases pore-water pressure in the soil/ rock mass and may cause landslides. Therefore, the precipitation, pore-water pressure and displacement show the kinematic variation of a landslide. In recent years, several approaches have been proposed to investigate the kinematic behavior. Among them, the combination of time series data and geotechnical model has demonstrated its effectiveness in stage classification of landslide kinematics. This paper aims to analyze and classify the stages of landslide kinematic variation with integrating weather and geotechnical monitoring data of Tan Son market landslides. The real-time monitoring station for Tan Son slow-moving landslides were equipped with fixed-in-place inclinometer probes and standard pore-water pressure sensors. The results show that landslide kinematics consist of three stages: stabilization, accumulation, and displacement. The displacement of the landslides was heterogeneous process. The velocity of the landslides significantly increased when the pore-water pressure ratio (ru) at the sliding surfaces > 0.53. While it is almost impossible to notice any displacement when the ratio ru < 0.45. During the displacement state, the trend of inverse velocity variation gradually decreases to near zero hour/mm. In addition, early warnings of landslide can be released based on the kinematic stages and changes in inverse velocity in this study.
{"title":"Analysis of landslide kinematics integrating weather and geotechnical monitoring data at Tan Son slow moving landslide in Ha Giang province","authors":"Duc Dao Minh, Minh Vu Cao, Yen Hoang Hai, Loc Nguyen The, Duc Do Minh","doi":"10.15625/2615-9783/18204","DOIUrl":"https://doi.org/10.15625/2615-9783/18204","url":null,"abstract":"Rainfall infiltration on the slope increases pore-water pressure in the soil/ rock mass and may cause landslides. Therefore, the precipitation, pore-water pressure and displacement show the kinematic variation of a landslide. In recent years, several approaches have been proposed to investigate the kinematic behavior. Among them, the combination of time series data and geotechnical model has demonstrated its effectiveness in stage classification of landslide kinematics. This paper aims to analyze and classify the stages of landslide kinematic variation with integrating weather and geotechnical monitoring data of Tan Son market landslides. The real-time monitoring station for Tan Son slow-moving landslides were equipped with fixed-in-place inclinometer probes and standard pore-water pressure sensors. The results show that landslide kinematics consist of three stages: stabilization, accumulation, and displacement. The displacement of the landslides was heterogeneous process. The velocity of the landslides significantly increased when the pore-water pressure ratio (ru) at the sliding surfaces > 0.53. While it is almost impossible to notice any displacement when the ratio ru < 0.45. During the displacement state, the trend of inverse velocity variation gradually decreases to near zero hour/mm. In addition, early warnings of landslide can be released based on the kinematic stages and changes in inverse velocity in this study.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49559432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.15625/2615-9783/17914
Huu Duy Nguyen
Precise streamflow prediction is crucial in the optimization of the distribution of water resources. This study develops the machine learning models by integrating recurrent gate unit (GRU) with bacterial foraging optimization (BFO), gray wolf optimizer (GWO), and human group optimization (HGO) to forecast the streamflow in the Tra Khuc River, Vietnam. For this purpose, the time series of daily rainfall and river flow at Son Giang station from 2000 to 2020 were employed to forecast the streamflow. The statistical indices, namely the root mean square error, the mean absolute error, and the coefficient of determination (R²), was utilized to evaluate the performance of the proposed models. The results showed that the three optimization algorithms (HGO, GWO, and BFO) effectively enhanced the performance of the GRU model. Moreover, among the four models (GRU, GRU-HGO, GRU-GWO, and GRU-BFO), the GRU-GWO model outperformed the other models with R² = 0.883. GRU-HGO achieved R² = 0.879, and GRU-BFO achieved R²=0.878. The results of this study showed that GRU combined with optimization algorithms is a reliable modeling approach in short-term flow forecasting.
{"title":"Daily streamflow forecasting by machine learning in Tra Khuc river in Vietnam","authors":"Huu Duy Nguyen","doi":"10.15625/2615-9783/17914","DOIUrl":"https://doi.org/10.15625/2615-9783/17914","url":null,"abstract":"Precise streamflow prediction is crucial in the optimization of the distribution of water resources. This study develops the machine learning models by integrating recurrent gate unit (GRU) with bacterial foraging optimization (BFO), gray wolf optimizer (GWO), and human group optimization (HGO) to forecast the streamflow in the Tra Khuc River, Vietnam. For this purpose, the time series of daily rainfall and river flow at Son Giang station from 2000 to 2020 were employed to forecast the streamflow. The statistical indices, namely the root mean square error, the mean absolute error, and the coefficient of determination (R²), was utilized to evaluate the performance of the proposed models. The results showed that the three optimization algorithms (HGO, GWO, and BFO) effectively enhanced the performance of the GRU model. \u0000Moreover, among the four models (GRU, GRU-HGO, GRU-GWO, and GRU-BFO), the GRU-GWO model outperformed the other models with R² = 0.883. GRU-HGO achieved R² = 0.879, and GRU-BFO achieved R²=0.878. The results of this study showed that GRU combined with optimization algorithms is a reliable modeling approach in short-term flow forecasting.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47863185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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