A framework for flood depth using hydrodynamic modeling and machine learning in the coastal province of Vietnam

regression (R2=0.7). This integration of hydrodynamic modeling and machine learning complements the framework much of the existing literature. It can provide decision-makers and local authorities with an advanced flood warning tool and contribute to improving sustainable development strategies in this and similar regions. Keywords: Flood depth, machine learning, hydrodynamics, Quang Tri, Vietnam. Introduction Flood is one of the most common natural disasters and, every year, causes significant *Corresponding author, Email: nguyenhuuduy@hus.edu.vn 456 damage to economies, injury, and loss of life (Hens et al., 2018; Shafizadeh-Moghadam et al., 2018; Nguyen, 2022). According to EM-DAT data, approximately 175,000 people have died, and 2.2 billion have been affected