This study aims to establish the optimal regression model for predicting total suspended solids (TSS) and Turbidity based on in situ data and spectral regions of Sentinel-2 images. Various machine learning models were evaluated, including Multilayer Perceptron Regression (MLPR), Random Forest Regression (RFR), AdaBoost Regression (ABR), Multiple Linear Regression (MLR), and K-Nearest Neighbors Regression (KNNR). These models were applied to different band combinations of spectral regions: visible (VIS), near-infrared (NIR), shortwave-infrared (SWIR), VIS+NIR (VNIR), and VIS+NIR+SWIR (VNIR+SWIR). The study results revealed that the MLR model, while not the best performer during training (R2 = 0.89 for TSS and R2 = 0.66 for turbidity), did not exhibit overfitting, with corresponding R² values in testing being 0.80 and 0.42, respectively. Variable selection for MLR models identified optimal spectral bands: B3, B5, B6, B8, B11, and B12 for TSS, and B4, B8, B11, and B12 for Turbidity. The final no-intercept multiple linear regression models achieved R2 = 0.88 for TSS and R2 = 0.62 for turbidity. Performance metrics for TSS were superior, with lower MAE, MSE, and RMSE compared to Turbidity. This study underscores the efficacy of using MLR models with selected spectral bands for accurate and generalizable predictions of TSS and turbidity.

Land use/land cover (LULC) and climate changes are two main factors directly affecting hydrologic conditions. However, very few studies in Vietnam have investigated changes in hydrological process under the impact of climate and land use changes on a basin scale. The objective of this study is to assess the individual and combined impacts of land use and climate changes on hydrological processes for the Nam Rom river basin, Northwestern Viet Nam using Remote Sensing (RS) and Soil and Water Assessment Tools (SWAT) model. SWAT model was used for hydrological process simulation. Results indicated that SWAT proved to be a powerful tool in simulating the impacts of land use and climate change on catchment hydrology. The change in historical land use between 1992 and 2015 strongly contributed to increasing hydrological processes (ET, percolation, ground water, and water yield), whereas, climate change led to significant decrease of all hydrological components. The combination of land use and climate changes significantly reduced surface runoff (-16.9%), ground water (-5.7%), water yield (-9.2%), and sediment load (-4.9%). Overall climatic changes had more significant effect on hydrological components than land use changes in the Nam Rom river basin during the 1992–2015. Under impacts of projected land use and climate change scenarios in 2030 on hydrological process of the upper Nam Rom river basin indicate that ET and surface flow are more sensitive to the changes in land use and climate in the future. In conclusion, the findings of this study will basic knowledge of the effects of climate and land-use changes on the hydrology for future development of integrated land use and water management practices in Nam Rom river basin.

The rapid urbanization of Pleiku City, Vietnam, has led to a sharp increase in the demand for and prices of residential land, creating challenges for urban management and land valuation. This study aims to identify and quantify the key factors influencing residential land prices in Pleiku to provide a scientific basis for land use planning and smart urban development. Data were collected through surveys of 30 state officials involved in land valuation and 250 households living along major streets in Pleiku. Cronbach’s alpha was used to test the reliability of the collected data, and exploratory factor analysis (EFA) was used to identify influencing factor groups. The results show that residential land prices are strongly influenced by multiple factors, with location and infrastructure playing the most decisive roles. Market land prices were found to be approximately 1.5–2 times higher than state-regulated prices. Among the identified factor groups, location and infrastructure had the strongest influence, followed by economic, social, legal, and specific land use factors. Price differences between land plots mainly reflect variations in location, street characteristics, accessibility, and commercial potential. The study concludes that location and infrastructure development are the dominant drivers of residential land prices in Pleiku. These findings have important implications for land valuation, urban planning, and the implementation of smart urban construction policies in rapidly developing cities in Vietnam.

Flooding remains a catastrophic natural hazard causing severe socio-economic damage globally and within Vietnam. Accurate flood susceptibility mapping (FSM) is critical for mitigation strategies, yet conventional machine learning applications often suffer from multicollinearity, sub-optimal hyperparameter tuning, and lack of model interpretability. This study develops a robust, interpretable, and automated FSM framework by integrating gradient boosting machines, including XGBoost, LightGBM, and categorical boosting (CatBoost), with the Optuna optimization framework and SHapley Additive exPlanations (SHAP). Focus was directed at the flood-prone mountainous districts of western Ha Tinh Province, North-Central Vietnam. A flood inventory was constructed using a Sentinel-1 radar change-detection approach via Google Earth Engine (GEE) across three historical flood events (in 2018, 2019, and 2020). Ten conditioning factors were validated using Pearson correlation and variance inflation factor assessments to prevent data redundancy. Experimental results showed that all three models achieved exceptional predictive power, with testing accuracies exceeding 0.95 and AUC values above 0.97. Notably, the Optuna-optimized CatBoost model outperformed the other classifiers, securing the highest testing accuracy (0.9583) and AUC (0.9807). Global and local SHAP analyses decoded the models’ black-box decisions, revealing elevation (ELE) as the overarching governor of flood distribution, while highlighting that normalized difference built-up index and density of road significantly amplify runoff. Conversely, normalized difference vegetation index acts as a critical mitigator. The generated maps isolated high to very high risk zones across 24%–26% of the territory, concentrated in low-lying, urbanized plains. These highly accurate outputs provide transparent, actionable spatial intelligence to support sustainable land-use zoning and resilient disaster risk governance.

1. Understanding the interconnected impacts of land use/land cover change (LULCC) and climate change (CC) on hydrological systems is increasingly important in rapidly developing tropical basins (So...