水电项目对越南硒圣河地区社会经济发展及环境保护的影响

硒圣河是湄公河的主要分支之一,其集水区地形条件和水资源量均对水电开发十分有利。此外,无论从物种多样性还是生物量来看,硒圣河水生生物资源非常丰富。鱼类是当地人们的重要食物来源,粗略估计鱼类物种数量可能在200-300种之间,目前具有详细记录的鱼类大约为120种。截至目前,从越南境内硒圣河上游至越-柬边界总共开发了6个水电项目,兴建了一座4A级硒圣调蓄水库。流经柬埔寨的硒圣河部分,依据发展规划将兴建3座水电站,即硒圣5号电站（硒圣1号电站以下）、硒圣3号电站、硒圣2号电站。硒圣河水电项目对区域（越南和柬埔寨）社会经济的影响体现在：为地处偏远和困难的集水区创造了新的能源资源,改善了交通系统和农村地区的生活条件。与此同时,这些水电项目也对该区域自然环境产生了重大影响。例如,改变了下游流动机理变化,导致下游河道淤积,对沿河生态系统也产生了一定影响。     

Predicting water flow in fully and partially saturated porous media: a new fractal-based permeability model

Hydrogeology Journal - Predicting the permeability of porous media in saturated and partially saturated conditions is of crucial importance in many geo-engineering areas, from water resources to...     

Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.     

Investigating the hydromechanical behaviour of bentonite pellets by swelling pressure tests and discrete element modelling

Acta Geotechnica - Bentonite pellet mixtures are candidate material for the sealing of galleries in radioactive waste disposals. The hydromechanical behaviour of assemblies of bentonite pellets is...     

Investigating the impacts of typhoon-induced floods on the agriculture in the central region of Vietnam by using hydrological models and satellite data

Flooding associated with landing tropical cyclones (TCs) is one of the major natural hazards in the coastal region of Vietnam. Annually, approximately 5 or 6 TCs make landfall in Vietnam, bringing heavy rains and inducing flooding, particularly to the central coastal region because of its topography and geographic configuration. This study focuses on the modelling of typhoon-induced floods that have resulted in widespread damage to agriculture over the central Thua Thien Hue Province of Vietnam by coupling two well-known hydrological models, KINEROS2 and HEC-RAS (Daniel et al. in Open Hydrol J 5(1), 2011), and using GSMaP (Global Satellite Mapping of Precipitation) data as the satellite rainfall input. Landsat imagery and GIS are also used for mapping and analysing the inundated areas. The discharge and water level from the KINEROS2 and HEC-RAS models displayed acceptable results for the floods modelled from three selected typhoons; both the Nash–Sutcliffe simulation efficiency coefficient (NSE) and the coefficient of determination (R²) were greater than 0.6. The simulated inundation maps of these typhoon-induced floods were compared with those extracted from the Landsat imagery to assess consistency. The result revealed a similar spatial extension of the inundated agricultural areas. This information, together with the forecasted TC movements and associated rainfalls, will be helpful to plan methods for mitigating potential typhoon-induced flooding and damage, particularly damage to agricultural regions.     

Pressure transient technique to constrain CO<Subscript>2</Subscript> plume boundaries

Carbon dioxide (CO₂) has been injected in the subsurface permeable formations as a means to cut atmospheric CO₂ emissions and/or enhance oil recovery (EOR). It is important to constrain the boundaries of the CO₂ plume in the target formation and/or other formations hosting the CO₂ migrated from the target formation. Monitoring methods and technologies to assess the CO₂ plume boundaries over time within a reservoir of interest are required. Previously introduced methods and technologies on pressure monitoring to detect the extent of the CO₂ plume require at least two wells, i.e. pulser and observation wells. We introduce pressure transient technique requiring single well only. Single well pressure transient testing (drawdown/buildup/injection/falloff) is widely used to determine reservoir properties and wellbore conditions. Pressure diagnostic plots are used to identify different flow regimes and determine the reservoir/well characteristics. We propose a method to determine the plume extent for a constant rate pressure transient test at a single well outside the CO₂ plume. Due to the significant contrast between mobility and storativity of the CO₂ and native fluids (oil or brine), the CO₂ boundary causes deviation in the pressure diagnostic response from that corresponding to previously identified heterogeneities. Using the superposition principle, we develop a relationship between the deviation time and the plume boundary. We demonstrate the applicability of the proposed method using numerically generated synthetic data corresponding to homogeneous, heterogeneous, and anisotropic cases to evaluate its potential and limitations. We discuss ways to identify and overcome the potential limitations for application of the method in the field.     

GNSS receiver implementations to mitigate the effects of commensurate sampling frequencies on DLL code tracking

The sampling frequency of a digitized intermediate frequency signal has a strong effect on the measurement accuracy of Global Navigation Satellite System (GNSS) receivers. The delay-locked loop tracking error is significant when the sampling frequency is an integer multiple of the code chipping rate, the so-called commensurate sampling frequency, and the number of distinct instantaneous residual code phases is low. This results in distortions of the correlation shape and discriminator functions that lead to a significant accuracy degradation. These effects are most pronounced when the sampling frequency is low. Notwithstanding, it is generally good for receivers to keep the sampling frequency to a minimum owing to the processing load and power consumption. It creates a challenge for existing GNSS signal processing techniques. Random, sine and sawtooth jitters have been found to mitigate these distortions considerably. A software algorithm and two hardware receiver implementations of these solutions are proposed. A register-based architecture can be directly applied to the conventional receiver architecture, while the increase in resource and power consumption is insignificant. A RAM-based design cannot only considerably minimize utilized resources but also slightly reduce the power consumption compared to the conventional architecture.     

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.     

Subduction initiation of Indochina and South China blocks: insight from the forearc ophiolitic peridotites of the Song Ma Suture Zone in Vietnam

The Song Ma region, which is located in the northwestern Vietnam represents the zone of amalgamation between Indochina and South China blocks. Numerous scattered ultramafic rocks occur in this region in association with Early to Middle Palaeozoic greenschists and paragneisses, and all these rocks were subjected to hydrous metamorphism and deformation. Here, we present new field data, mineral chemistry and geochemistry from a suite of hydrated peridotites within the Song Ma region and discuss the tectonic significances of the region. We also combine the available data within the Song Ma region and Indochina–South China blocks to discuss the tectonic evolution of the subduction zone. Based on the results, we suggest that the peridotites from the Song Ma are mantle residues that suffered a high degree of partial melting in a forearc tectonic setting. The present data together with the available data within the Song Ma region and the Indochina and South China blocks clearly represent a southward directed Middle Palaeozoic subduction system. The Middle Palaeozoic subduction and accretion events mark the evolutionary history along an active convergent margin between the Indochina and South China blocks, possibly related to the amalgamation of the Pangaea supercontinent. Copyright © 2015 John Wiley & Sons, Ltd.     

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.