Abstract

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.

摘要

本研究的目的是使用自适应增强 (AdaBoost)、增强广义线性模型 (BGLM)、极端梯度增强 (XGB) 集成模型和深度决策树的新型集成框架包括深度提升 (DB) 模型。为此，在洪水灾害建模中使用了 14 个洪水调节变量作为自变量。此外，通过实地考察和可用的洪水信息确定了该地区的 130 个洪水点，这些信息被用作建模中的因变量。结果表明，所有使用的模型都具有良好的洪水灾害预测效率。BGLM、XGB、AdaBoost和DB模型的曲线下面积（AUC）分别为0.88、0.87、0.89和0.91，这表明 DB 模型在研究区洪水灾害建模中的效率最高。变量的相对重要性表明它们在每个模型中都有不同的影响。与河流的高度和距离比其他变量更重要。然而，基于机器学习模型，这两个变量被选为最重要的变量，但其他变量可能对洪水灾害有影响。