Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta.

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt2) is lower than that of Landsat estimation (10.4 ppt2) and numerical simulations (9.6 ppt2). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.

A novel flood risk management approach based on future climate and land use change scenarios.

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta.

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam's Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R2 = 0.931, RMSE = 0.055), XGR-MSA (R2 = 0.928, RMSE = 0.06), XGR-BSA (R2 = 0.926, RMSE = 0.062), XGR-SSA (R2 = 0.917, 0.07), XGR-PSO (R2 = 0.916, RMSE = 0.08), XGR (R2 = 0.867, RMSE = 0.1), CatBoost (R2 = 0.78, RMSE = 0.12), and RF (R2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.

Sequencing and analysis of the mitochondrial genome of Pennahia anea (Bloch, 1793) (Perciformes: Sciaenidae).

Donkey croaker, Pennahia anea (Bloch, 1793) is a commercially important croaker in the Indo-Pacific region. In this study, we sequenced and analyzed the mitogenome of P. anea. The nearly complete mitochondrial genome of P. anea is 15,694 bp in size. It contained 13 protein-coding genes (PCGs), 2 rRNA genes, and 22 tRNA genes. The sequence had the A-T content of 55.4% and GC content of 44.6%. All 13 PCGs used ATG codon for initiation, while TAA codon was the most common for termination. Phylogenetic analysis demonstrated that P. anea is located within the genus Pennahia. This study provides additional data for the understanding of the phylogeny of the family Sciaenidae.

DNA barcoding of coastal ray-finned fishes in Vietnam.

DNA barcoding based on a fragment of the cytochrome c oxidase subunit I (COI) gene is widely applied in species identification and biodiversity studies. The aim of this study was to establish a comprehensive barcoding database of coastal ray-finned fishes in Vietnam. A total of 3,638 specimens were collected from fish landing sites in northern, central and southern Vietnam. Seven hundred and sixty-five COI sequences of ray-finned fishes were generated, belonging to 458 species, 273 genera, 113 families and 43 orders. A total of 59 species were newly recorded in Vietnam and sequences of six species were new to the Genbank and BOLD online databases. Only 32 species cannot be annotated to species level because difficulty in morphological identifications and their Kimura-2-Parameter (K2P) genetic distances to most similar sequences were more than 2%. Moreover, intra-specific genetic distances in some species are also higher than 2%, implying the existence of putative cryptic species. The mean K2P genetic distances within species, genera, families, orders and classes were 0.34%, 12.14%, 17.39%, 21.42%, and 24.80, respectively. Species compositions are quite different with only 16 common species among northern, central and southern Vietnam. This may attribute to multiple habitats and environmental factors across the 3,260 km Vietnamese coastline. Our results confirmed that DNA barcoding is an efficient and reliable tool for coastal fish identification in Vietnam, and also established a reliable DNA barcode reference library for these fishes. DNA barcodes will contribute to future efforts to achieve better monitoring, conservation, and management of fisheries in Vietnam.