Modeling and Mapping of Flood Susceptibility at Que Son District, Quang Nam Province, Vietnam using CatBoost

In this research, the main objective is to model and map flood susceptibility in Que Son district, Quang Nam province, Vietnam using one of the effective machine learning model namely CatBoost. With this purpose, a total of 96 flood and non-flood locations and a set of 10 conditioning factors were collected to construct the geospatial database. Thereafter, Shap feature importance method was used to validate and select the most important conditioning factors used for modeling of flood susceptibility, and the results showed that only 8 conditioning factors including aspect, slope, curvature, elevation, land cover, rainfall, distance to rivers, and Topographic Wetness Index (TWI) were selected for final modelling of flood susceptibility at the study area. Validation of the model was also done using various statistical indexes including area under the ROC curve (AUC). Validation results showed that the performance of CatBoost model (AUC = 0.96 for training and AUC = 0.94 for testing) is good for prediction of flood susceptibility of the study area. Thus, it can be concluded that CatBoost is valuable tool for flood susceptibility modeling which can be used to assess flood susceptibility in other flood prone areas of the world. In addition, flood susceptibility map generated from CatBoost model in this study might be helpful in development of better flood mitigation strategies at the study area.