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Luu  Chinh  Bui  Quynh Duy  Costache  Romulus  Nguyen  Luan Thanh  Nguyen  Thu Thuy  Van Phong  Tran  Van Le  Hiep  Pham  Binh Thai 《Natural Hazards》2021,108(3):3229-3251
Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...  相似文献   
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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.  相似文献   
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Degradation of groundwater quality by human activities is a widespread environmental problem in Vietnam. Groundwater there is a major source of water for domestic and industrial purposes. This paper reviews, compiles, and comprehensively analyzes spatiotemporal variations of hydrological and hydrogeological characteristics of shallow and deep groundwater aquifers in northern Hanoi industrial zones and in nearby Red River water. Groundwater level, electrical conductivity, and water temperature were measured in six monitoring wells, complemented by anion, cation, and stable isotope analyses of ground and surface water. The results show that the groundwater in both shallow and deep aquifers was fresh, but mainly calcium-bicarbonate type contaminants and human activities affect groundwater and surface water composition. With the goal of devising sustainable water use regulations, more research must be directed toward long-term monitoring of groundwater and surface water quality, as well as toward detailed investigation of the hydraulic characteristics of local aquifers in the study area.  相似文献   
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Currently, densities of Enterococcus in marine bathing beach samples are performed using conventional methods which require 24 h to obtain results. Real-time PCR methods are available which can measure results in as little as 3 h. The purpose of this study was to evaluate a more rapid test method for the determination of bacterial contamination in marine bathing beaches to better protect human health. The geometric mean of Enterococcus densities using Enterolert® defined substrate testing and membrane filtration ranged from 5.2 to 150 MPN or CFU/100 mL and corresponding qPCR results ranged from 6.6 to 1785 CCE/100 mL. The regression analysis of these results showed a positive correlation between qPCR and conventional tests with an overall correlation (r) of 0.71. qPCR was found to provide accurate and sensitive estimate of Enterococcus densities and has the potential to be used as a rapid test method for the quantification of Enterococcus in marine waters.  相似文献   
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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.  相似文献   
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To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.  相似文献   
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The VATLY radio telescope, operating at 1.415 GHz in Ha Noi, has been used to track the Sun in the summer?–?autumn months in 2012. Evidence has been obtained for solar activity, including occasional flares and variable oscillations with amplitudes at the percent level and periods of about 6 min. Comparison with data collected at the same frequency by the Learmonth Observatory in Australia suggests that the observed oscillations were associated with solar activity. A joint analysis of both data sets is presented, evaluating the correlations between them. We describe the common and different main features.  相似文献   
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