Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.

This study assesses greenhouse gas (GHG) emissions and potential mitigation pathways for Viet Nam’s metallurgical industry, covering both ferrous and non-ferrous sectors. Emissions were estimated for the period 2015 – 2019 following the 2006 IPCC Guidelines, using national industrial statistics, enterprise energy reports, and detailed surveys conducted at major production facilities such as the Southern Steel Plant and Thai Nguyen Iron and Steel Joint Stock Company. Two scenarios were projected for 2025 and 2030: a Baseline Scenario, assuming continuation of current practices, and a Mitigation Scenario, which integrates technical and managerial measures including energy efficiency improvements, adoption of electric arc furnaces (EAF), waste heat recovery, fuel switching, deployment of innovative technologies (H₂-DRI, CCUS), and increased recycling of secondary metals. Results indicate that ferrous metallurgy accounts for over 90% of sectoral emissions, dominated by BF – BOF processes, while non-ferrous metallurgy especially aluminum production is experiencing rapid growth, with emissions projected to exceed 8 million tCO₂e by 2030. The proposed measures could reduce industry emissions by 20 – 35% by 2030 relative to the baseline. Based on these findings, a preliminary framework for Measurement, Reporting, and Verification (MRV) is suggested to enhance transparency and support integration of the metallurgical sector into Viet Nam’s national climate strategies and emerging carbon market mechanisms.