Vol.:(0123456789) Natural Hazards https://doi.org/10.1007/s11069-021-04821-7 1 3 ORIGINAL PAPER Flood‑prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam Chinh Luu 1  · Quynh Duy Bui 2  · Romulus Costache 3  · Luan Thanh Nguyen 4  · Thu Thuy Nguyen 5  · Tran Van Phong 6  · Hiep Van Le 7  · Binh Thai Pham 7 Received: 9 November 2020 / Accepted: 24 May 2021 © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract Vietnam’s central coastal region is the most vulnerable and always at food risk, severely afecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often afected by foods and storms over the year. However, it still lacks studies on food hazard estimation and prediction tools in this area. This study aims to develop a fooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); his- torical food marks; and available data of topography, hydrology, geology, and environ- ment considering Quang Binh province as a study area. We used food mark locations of major fooding events in the years 2007, 2010, and 2016; and ten food conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best perfor- mance in terms of food prediction with an accuracy higher than 92 %. The fnal food sus- ceptibility map highlights 6,265 km 2 (78.8 % area) with a very low fooding hazard, 391 km 2 (4.9 % area) with a low fooding hazard, 224 km 2 (2.8 % area) with a moderate fooding hazard, 243 km 2 (3.1 %) with a high fooding hazard, and 829 km 2 (10.4 % area) with very high fooding hazard. The fnal fooding susceptibility assessment map could add a valu- able source for food risk reduction and management activities of Quang Binh province. Keywords Flood susceptibility map · Alternating decision tree · Logistic model tree · Reduced-error pruning tree · J48 · Naïve Bayes tree * Chinh Luu luuthidieuchinh@nuce.edu.vn * Quynh Duy Bui quynhbd@nuce.edu.vn * Binh Thai Pham binhpt@utt.edu.vn Extended author information available on the last page of the article