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•Top ten influencing factors of groundwater salinization were identified.•The CatBoost Regression model provides the highest accuracy salinity prediction.•In the Mekong Delta, ...groundwater pumping has a strong impact on salinization processes.•Forty-eight percentage of the population is in threshold salinity areas (>250 mg/L).•Immediate actions are needed to prevent groundwater salinization.
Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta.
The hydrogeomorphology of the Vietnamese Mekong Delta (VMD) has been significantly altered by natural and anthropogenic drivers. In this study, the spatiotemporal changes of the flow regime were ...examined by analysing the long‐term daily, monthly, annual and extreme discharges and water levels from 1980 to 2018, supported by further investigation of the long‐term annual sediment load (from the 1960s to 2015), river bathymetric data (in 1998, 2014 and 2017) and daily salinity concentration (from the 1990s to 2015) using various statistical methods and a coupled numerical model. Then, the effects of riverbed incision on the hydrology were investigated. The results show that the dry season discharge (i.e., in March–June) of the Tien River increased by up to 23% from the predam period (1980–1992) to the postdam period (1993–2018) but that the dry season water level at My Thuan decreased by up to −46%. The annual mean and monthly water levels in June at Tan Chau and in January and June–October at My Thuan in the Tien River decreased statistically, even though the respective discharges increased significantly. These decreased water levels instead of the increased discharges were attributed to the accelerated riverbed incision upstream from My Thuan, which increased by more than three times, from a mean rate of −0.16 m/year (−16.7 Mm3/year) in 1998–2014 to −0.5 m/year (−52.5 Mm3/year) in 2014–2017. This accelerated riverbed incision was likely caused by the reduction in the sediment load of the VMD (from 166.7 Mt/year in the predam period to 57.6 Mt/year in the postdam period) and increase in sand mining (from 3.9 Mm3 in 2012 to 13.43 Mm3 in 2018). Collectively, the decreased dry season water level in the Tien River is likely one of the main causes of the enhanced salinity intrusion.
The hydrogeomorphology of the Vietnamese Mekong Delta has been significantly altered by natural and anthropogenic drivers. By analysing the long‐term discharge, water level, sediment, and bathymetric data, supported by a coupled 2D hydro‐sediment‐morpho dynamics model, we observed that riverbed incision was the main cause of the decreased dry season water levels in the Tien River, although the respective discharges increased. Consequently, the decreased water level was likely one of the main causes of the enhanced salinity intrusion.
Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for ...a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.
Abstract
To protect Ho Chi Minh City (HCMC) from submergence due to the ongoing rapid sea level rise (SLR), the Vietnamese government have proposed the construction of a sea dike in Can Gio Bay. Can ...Gio Bay will be closed to regulate the storage and to control water levels in the drainage and sewer systems of HCMC. This could significantly impact the salinity distribution in the Bay and affect its mangrove forest. In this study, a set of scenarios will be analyzed using two-dimensional hydrodynamic and convective-dispersive models to assess the effects of SLR and the construction of a sea dike on salinity distribution in the Bay. The results reveal that the salinity in most areas of the Bay tended to increase positively with the SLR. The sea dikes significantly reduced seawater intrusion into half of the upstream area of the Bay. Considering the influence of SLR and the construction of a sea dike, the sea dike could result in the reduction of salinity. Furthermore, if the sea dike was operated for a long time, half of the Bay area would become freshwater, which would lead to adverse effects on the mangrove forest.
Located at the downstream end of the Dong Nai-Saigon river basin, near the mouth of the Saigon River, which is known as the “green lungs” of Ho Chi Minh City (HCMC) due to the ecosystem functions of ...the Can Gio mangrove forest, is a very complex hydrodynamic and geomorphic region with crossing estuaries, forming the so-called Can Gio Bay. Given its flat, low-lying topography, this area is strongly influenced by two main factors: (1) upstream flooding and (2) tidal regimes. The historical flood event of 2000 indicated that the downstream region of HCMC suffers from serious flooding, and that the Can Gio area is the worst affected, with approximately 90% of its area being inundated. This research aims to investigate the impact of tides and inflows in Can Gio Bay in the context of sea level rise and a sea dike structure connecting Go Cong to Vung Tau. A two-dimensional hydrodynamic model combined with a wetting and drying scheme is used to determine the locations of inundated areas. This research also shows how sea level rise and upstream flows cause flooding in Can Gio Bay, and identifies the negative and positive impacts of sea dike construction on Can Gio Bay.
This study aims to propose a methodology for establishing the optimal rule curves of reservoir operation based on a multi-use reservoir system. Located on the upper Saigon River, Dau Tieng Reservoir ...plays an important role in economic and social aspects: (1) flood control; (2) domestic and industrial demands; (3) flushing out salt water intrusion from the downstream area; and (4) agriculture irrigation. We propose a reservoir operation model using a constrained genetic algorithm (CGA), in which the fitness function was constrained by penalty functions. The proposed model was formulated by including various water demands configured into the objective function. The penalty functions were designed for various constraints and integrated into the objectives of the operation process to perform the fitness function. The model’s performance was simulated for the last 20 years with 1-month intervals and evaluated through a generalized shortage index (GSI). The derived results of three CGA cases with associated environmental flow requirements significantly improved the efficiency and effectiveness of water supply capability to various water demands as compared to current operation. Among the three cases, CGA case 3 achieved much better water releases from the reservoir as indicated by a small derived
GSI
value (0.33), the smallest shortage of environmental water (0.11 m
3
/s) and the highest water usage (63.8 %). Thus, the derived results of CGA case 3 were presented as the best rule curves for reservoir operation. To summarize, CGA was demonstrated as an effective and powerful tool for optimal strategy searching for multi-use reservoir operations.
•CERES-Rice model is calibrated using a Robust Parameter Estimation (ROPE) method.•Rice growth in the Vietnam Mekong Delta is simulated well by the CERES-Rice model.•A two-stage calibration using ...ROPE is found effective with the CERES-Rice model.•Robust parameter ranges for two rice cultivars Jasmine 85 and VD20 are proposed.
Rice crop models, such as CERES-Rice, are useful tools to predict rice growth and understand the effects of climatic and management changes on rice yield. As these models involve complex processes, non-linearity and interdependence of parameters may result in high uncertainty in model results. In this study, we tested the CERES-Rice model in the Vietnam Mekong Delta for two rice cultivars (Jasmine 85 and VD20) in four experimental sites. We applied a two-stage calibration procedure. In the first stage, initial sets of good performing parameters were identified. In the second stage, the good performing parameter sets and their minimum-maximum ranges were refined using the Robust Parameter Estimation (ROPE) approach. We found that in the first calibration stage, the majority of the parameter sets that performed well in the calibration sites performed poorly in the validation sites. For example, the simulated rice yields were within +/−5% of the observed yields in the calibration sites. But in the validation sites, the same parameter sets resulted in the differences of −77% to 19% for Jasmine 85 and −47% to 9.5% for VD20. This is because these parameter sets had a broad range of values, which compensated each other well in calibration but were less successful to do so in validation. When the ROPE was applied, the value ranges of the parameters narrowed down and the model performance in validation improved. In general, the parameter ranges identified by ROPE are considered more robust and have higher probability to perform better. Our positive results show a good potential of the ROPE approach for calibration of the CERES-Rice model, which can also be applied with similar other crop growth models. The robust parameter value ranges derived in this study may be used as reference parameter values for future applications of the model in the region.
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