Application of Machine Learning, Remote Sensing, and GIS Models in Optimizing Disaster Risk Reduction Measures for Communities in the Northern Mountainous Region of Vietnam

This study applies machine learning models, remote sensing, and Geographic Information Systems (GIS) to optimize disaster risk reduction measures for communities in the northern mountainous region of Vietnam. The analysis results indicate that factors such as terrain, climate, population density, and infrastructure play a crucial role in determining disaster risks. Machine learning models are employed to classify and predict the risks of landslides, flash floods, and storms, achieving an accuracy of up to 85%. Mitigation measures such as resettlement, construction of protective infrastructure, infrastructure improvement, and community awareness enhancement have been proposed and proven effective in reducing damage. This research contributes to the development of advanced methods in disaster risk management in Vietnam.