https://dspace.ctu.edu.vn/jspui/handle/123456789/8851 Tổng cục Khí tượng thủy văn 2026-04-22T21:23:08Z https://dspace.ctu.edu.vn/jspui/handle/123456789/124645 Title: Application of ozone technology in leachate treatment for sustainable development: A brief review Authors: Luu, Thi Cuc; Hoang, Van Hung; Van, Huu Tap Abstract: This paper provides an overview of ozone technology in leachate treatment, including its characteristics, sources, treatment efficiency, and associated challenges. The study highlights the effectiveness of ozone-based methods in removing major pollutants such as chemical oxygen demand (COD) and ammonium nitrogen (NH₄⁺-N) from landfill leachate, achieving removal efficiencies of up to 99.8% for COD and 91.14% for NH₄⁺-N under optimized experimental conditions. However, several limitations remain, including high energy consumption, the potential formation of harmful by-products (e.g., bromates and aldehydes), and challenges related to regulatory compliance. The review also summarizes recent advances in ozone-assisted leachate treatment and identifies key barriers to large-scale application, with particular emphasis on Vietnam. Currently, Vietnam generates approximately 4.3 million tons of municipal solid waste annually, of which more than 70% is disposed of in landfills, while leachate treatment remains inadequate, leading to serious environmental impacts. This study adopts a systematic approach, integrating data analysis, result synthesis, and comparative evaluation of studies published between 2014 and 2025. The findings provide a valuable reference for researchers and policymakers concerned with sustainable leachate management and the application of ozone technology, thereby offering a clear basis and direction for future research. 2025-01-01T00:00:00Z https://dspace.ctu.edu.vn/jspui/handle/123456789/124644 Title: Integrating Dempster Shafer theory, certainty factors and topographic indices for landslide susceptibility analysis in Ha Quang district, Cao Bang province Authors: Phan, Thi Mai Hoa; Tran, Hong Hanh; Nguyen, Quoc Phi; Pham, Thi Thanh Hoa Abstract: Ha Quang District, located within the Non Nuoc Cao Bang Geopark, is an area of high geological, ecological, and cultural significance and has been recognized by UNESCO as a member of the Global Geoparks Network. However, the region is highly susceptible to geological hazards, particularly landslides. This study applies Dempster-Shafer (DS) theory and the Certainty Factor (CF) method to evaluate landslide susceptibility using a Geographic Information System (GIS). A total of 196 landslide events were identified from historical records, Google Earth imagery, and field surveys to establish a landslide inventory map. Seven conditioning factors, including slope, Topographic Roughness Index (TRI), Topographic Wetness Index (TWI), Stream Power Index (SPI), Mass Balance Index (MBI), Normalized Difference Vegetation Index (NDVI), and rainfall, were integrated into the analysis. The belief map generated from the DS model was evaluated using receiver operating characteristic (ROC) analysis and the area under the curve (AUC), achieving an overall prediction accuracy of 74.5%. For comparison, the CF model yielded a success rate of 67%. The results demonstrate that the DS approach provides superior predictive performance by effectively handling uncertainty, thereby improving landslide susceptibility assessment compared to traditional GIS-based methods. The findings provide a valuable scientific basis for landslide risk mitigation, land use planning, and sustainable infrastructure development in mountainous regions. 2025-01-01T00:00:00Z https://dspace.ctu.edu.vn/jspui/handle/123456789/124643 Title: Morphological changes at Nhat Le estuary under hydro meteorological influence Authors: Vu, Dinh Cuong; Nguyen, Thanh Hung; Tran, Dinh Hoa Abstract: Nhat Le estuary is located on the central coast of Viet Nam and is characterized by strong morphological changes under the influence of seasonally varying dynamic factors. Based on the analysis of shoreline and topographic data, this study examines the relationship between hydro-meteorological conditions and morphological changes at the Nhat Le estuary. The results indicate that: (1) seasonal variations in wave conditions and river discharge have a significant influence on estuarine morphology. The width of the river mouth and the arrow-shaped sandspit on the southern bank are primarily affected by river discharge, whereas the main channel and sandbars are strongly controlled by wave dynamics; and (2) these interactions lead to the development of a generalized conceptual diagram describing the morphological change cycle of the Nhat Le estuary under the combined influence of wave action and river flow. The findings contribute to a clearer understanding of the causes and trends of morphological evolution and provide an important scientific basis for proposing solutions to stabilize the Nhat Le estuary. 2025-01-01T00:00:00Z https://dspace.ctu.edu.vn/jspui/handle/123456789/124642 Title: Comparison of random forest and extreme gradient boosting algorithms in land cover classification in Van Yen district, Yen Bai province, Vietnam Authors: Khuc, Thanh Dong; Luong, Ngoc Dung; Dang, Dieu Hue; Tran, Anh Van Abstract: Land cover classification using remote sensing data plays a crucial role in resource management and environmental monitoring. This study compares the performance of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms in land cover classification in Van Yen District, Yen Bai Province, Vietnam. The input data include Sentinel-1 synthetic aperture radar imagery, Sentinel-2 optical imagery, and a total of 7,214 sample points used for model training and validation on the Google Colab platform. The results indicate that both RF and XGBoost achieve high classification performance, with overall accuracy ranging from 94.8% to 96.3% and Kappa coefficients between 0.936 and 0.955. Notably, RF demonstrates greater stability and consistently higher accuracy than XGBoost in both scenarios: using Sentinel-2 data alone and combining Sentinel-2 with Sentinel-1 data. The findings provide a scientific basis for selecting suitable algorithms and data sources to improve land cover classification efficiency in the study area. 2025-01-01T00:00:00Z