Citation: Pham Quang, M.; Tallam, K. Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach. Sustainability 2022, 14, 11861. https://doi.org/10.3390/ su141911861 Academic Editors: Saqib Iqbal Hakak and Thippa Reddy Gadekallu Received: 1 August 2022 Accepted: 13 September 2022 Published: 21 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach Minh Pham Quang 1, * and Krti Tallam 2, * 1 VNU-HCM High School for the Gifted, Ho Chi Minh City 70000, Vietnam 2 Department of Biology, Stanford University, Stanford, CA 94305, USA * Correspondence: minhquangpham6@gmail.com (M.P.Q.); ktallam7@stanford.edu (K.T.) Abstract: Flooding as a hazard has negatively impacted Vietnam’s agriculture, economy, and infras- tructure 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 char- acteristics: 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. Keywords: flood risk assessment; artificial neural networks; natural hazards; machine learning; flood forecasting 1. Introduction Situated in Southeast Asia, Vietnam has been affected by natural hazards and in particular, floods. Vietnam is ranked sixth in the highest climate-risk countries during the period of 1999 to 2018 [1] and ranked fifth in countries most prone to flood risk [2]. With climate change intensifying extreme weather patterns, Vietnam has been increasingly impacted by more erratic storms and floods. In particular, the 2020 monsoon season brought about intense flooding, causing 100 deaths and flooding thousands of homes in the Hue province, central Vietnam, and many crops and infrastructure were destroyed [3]. Therefore, it is of interest for Vietnam’s policymakers to develop a highly efficient flood mitigation system. Flood risk is a highly complex factor which involves both natural and socio-economic elements [4,5]. Flood risk can be defined as the probability of being exposed to potential flood hazards. These hazards can come in the form of fluvial flood (river flood), pluvial flood (rainfall flood), and coastal flood. The occurrences of these process are facilitated through natural processes such as torrential rain, typhoons, and storm surges. Moreover, we can note the influence of urbanization in Vietnamese cities on the increased vulnerability of its citizens to urban flooding [6]. Given that Vietnam is exposed to a variety of flood hazards, we considered all types of flood hazards as flood risks in this study. As a result, policymakers need to identify interactions between natural risks and other societal risk factors in response to flood damage [7]. Vietnam puts flood control management as Sustainability 2022, 14, 11861. https://doi.org/10.3390/su141911861 https://www.mdpi.com/journal/sustainability