Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach

Abstract

Flooding as a hazard has negatively impacted Vietnam’s agriculture, economy, and infrastructure with increasing intensity because of climate change. Flood hazards in Vietnam are difficult to combat, as Vietnam is densely populated with rivers and canals. While there are attempts to lessen the damage through hazard mitigation policies, such as early evacuation warnings, these attempts are made heavily reliant on short-term traditional statistical models and physical hydrology modeling, which provide suboptimal results. The current situation is caused by the fragmented approach from the Vietnamese government and exacerbates a need for more centralized and robust flood predictive systems. Local governments need to employ their own prediction models which often lack the capacity to draw key insights from limited flood occurrences. Given the robustness of machine learning, especially in low data settings, in this study, we attempt to introduce an artificial neural network model with the aim to create long-term forecast and compare it with other machine learning approaches. We trained the models using different variables evaluated under three characteristics: climatic, hydrological, and socio-economic. We found that our artificial neural network model performed substantially better both in performance metrics (91% accuracy) and relative to other models and can predict well flood hazards in the long term.