Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

• Main Authors: Quoc Bao Pham, Subodh Chandra Pal, Rabin Chakrabortty, Akbar Norouzi, Mohammad Golshan, Akinwale T. Ogunrinde, Saeid Janizadeh, Khaled Mohamed Khedher, Duong Tran Anh
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Geomatics, Natural Hazards & Risk
• Subjects: boosting ensemble model; deep boosting (db); flood hazard; deep decision tree; talar watershed
• Online Access: http://dx.doi.org/10.1080/19475705.2021.1968510

The purpose of the present study was to predict the areas affected by flood hazard in the Talar watershed, Mazandaran province, Iran, using Adaptive Boosting (AdaBoost), Boosted Generalized Linear Models (BGLM), Extreme Gradient Boosting (XGB) ensemble models, and the novel ensemble framework of deep decision trees include the Deep Boosting (DB) model. For this purpose, 14 flood conditioning variables were used as independent variables in flood hazard modeling. In addition, 130 flood points in the region were identified by field visits and available flood information, which were used as the dependent variable in modeling. The results showed that all used models have a good efficiency in predicting flood hazard. The area under curve (AUC) of BGLM, XGB, AdaBoost and DB models were 0.88, 0.87, 0.89 and 0.91, respectively, which indicated the highest efficiency of the DB model in flood hazard modeling in the study area. Relative importance of the variables showed that they have different effects in each model. Altitude and distance from the river are more important than other variables. However, these two variables have been selected as the most important variables based on machine learning models, but other variables may be influential in flood hazards.