ORCID Profile
0000-0002-6835-0416
Current Organisations
Universidade Nova de Lisboa Instituto de Tecnologia Química e Biológica
,
ThuyLoi University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: MDPI AG
Date: 26-04-2020
DOI: 10.3390/RS12091373
Abstract: Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.
Publisher: Springer Science and Business Media LLC
Date: 08-09-2021
DOI: 10.1007/S11356-021-16282-3
Abstract: Groundwater salinization is one of the most severe environmental problems in coastal aquifers worldwide, causing exceeding salinity in groundwater supply systems for many purposes. High salinity concentration in groundwater can be detected several kilometers inland and may result in an increased risk for coastal water supply systems and human health problems. This study investigates the impacts of groundwater pumping practices and regional groundwater flow dynamics on groundwater flow and salinity intrusion in the coastal aquifers of the Vietnamese Mekong Delta using the SEAWAT model-a variable-density groundwater flow and solute transport model. The model was constructed in three dimensions (3D) and accounted for multi-aquifers, variation of groundwater levels in neighboring areas, pumping, and paleo-salinity. Model calibration was carried for 13 years (2000 to 2012), and validation was conducted for 4 years (2013 to 2016). The best-calibrated model was used to develop prediction models for the next 14 years (2017 to 2030). Six future scenarios were introduced based on pumping rates and regional groundwater levels. Modeling results revealed that groundwater pumping activities and variation of regional groundwater flow systems strongly influence groundwater level depletion and saline movement from upper layers to lower layers. High salinity (>2.0 g/L) was expected to expand downward up to 150 m in depth and 2000 m toward surrounding areas in the next 14 years under increasing groundwater pumping capacity. A slight recovery in water level was also observed with decreasing groundwater exploitation. The reduction in the pumping rate from both local and regional scales will be necessary to recover groundwater levels and protect fresh aquifers from expanding paleo-saline in groundwater.
Publisher: Informa UK Limited
Date: 03-10-2019
DOI: 10.1080/10256016.2019.1673746
Abstract: The stable isotopes of oxygen and hydrogen can provide useful insights into water origin and hydrological processes. The present study aims to investigate the characteristics of stable H/O isotopes of groundwater and surface water in a coastal area of the Vietnamese Mekong Delta. Isotopes and chloride concentrations of surface water show a highly seasonal and linearly spatial variability, depending on the distance to the sea. The seasonal variation of upstream discharge and rainfall plays an important role in changes of the isotopic compositions and chloride concentrations. Tide also influences on chloride concentrations of surface water while it does not change the isotopic compositions. Evaporation plays a crucial role in changes of isotopic compositions, while the influence of freshwater/seawater mixing on isotopic variabilities is negligible. Groundwater has a spatial heterogeneity in isotopic compositions and chloride concentrations, reflecting different recharge sources and seawater intrusion processes. Groundwater in shallow aquifers originates from rainfall and surface water with small evaporative losses, and it experienced different magnitudes of mixing with seawater. Groundwater in deep aquifers might be recharged by open-surface water evaporation in the last glacial age with minor impacts of seawater intrusion on these aquifers.
Publisher: MDPI AG
Date: 20-08-2020
DOI: 10.3390/RS12172688
Abstract: Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Springer Science and Business Media LLC
Date: 20-08-2019
DOI: 10.1007/S10653-019-00400-9
Abstract: Groundwater is a primary freshwater source for various domestic, industrial and agricultural purposes, especially in coastal regions where there are lacking surface water supply. However, groundwater quality in coastal regions is often threatened by seawater intrusion and contamination due to both anthropogenic activities and natural processes. Therefore, insights into groundwater geochemistry and occurrences are necessary for sustainable groundwater management in coastal regions. The main aim of this study is to investigate the hydrogeochemical characteristics and their influencing factors in a coastal area of the Mekong Delta, Vietnam (MD). A total of 286 groundwater s les were taken from shallow and deep aquifers for analyzing major ions and stable isotopes. The results show that deep groundwater is dominated by Ca-HCO[Formula: see text], Ca-Na-HCO[Formula: see text], Ca-Mg-Cl, and Na-HCO[Formula: see text] while shallow groundwater is dominated by the Na-Cl water type. In this region, the main geochemical processes controlling groundwater chemistry are ion exchanges, mineralization and evaporation. Groundwater salinization in coastal aquifers of the Mekong Delta is caused by (1) paleo-seawater intrusion and evaporation occurring in the Holocene and Pleistocene aquifers, (2) dissolution of salt sediment/rock and leakage of saline from upper to lower aquifers due to excessive groundwater exploitation and hydraulic connection. High nitrate concentrations in both shallow and deep aquifers are related to human activities. These results imply that groundwater extraction may exacerbate groundwater quality-related problems and suitable solutions for sustainable groundwater management in the coastal area of the Mekong Delta are needed.
Location: Portugal
Location: Japan
No related grants have been discovered for Tran Dang An.