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.

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.

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.

The objective of this research was to provide hydrological and stable isotopic information to figure out the characteristics of surface water and groundwater in Cu Lao Dung (CLD) Island, a coastal area in the Mekong Delta, South of Vietnam. Water samples were collected in March 2013 during the driest period of the year. The basic tracing elements of water showed that groundwater was characterized by fresh water of Ca-HCO3 type, whilst almost river and canal water samples were categorized in saline water of Na-Cl type. Groundwater is only fresh water source for drinking purpose in this area, because river and canal water cannot be used for that due to serious seawater intrusion. Moreover, the results of inorganic solute concentration and stable isotopic compositions suggest that currently, interaction between river/canal water and groundwater may not occur in CLD Island. This implies that main recharge areas of the groundwater seem to be located outside of the CLD Island. For sustainable groundwater pumping, an appropriate management is necessary in the coastal areas in the Mekong Delta; more investigation, not only in small areas but also in large scale of Mekong Delta, is needed