Development of zoning and flood-forecasting models is essential for making optimal management decisions before and after floods. The Komijan watershed of Markazi Province, Iran is often affected by floods that have caused great material damage and loss of life. The main objective of this study is to use a new machine-learning method to create three models: best-first decision tree (BFT), a bagging best-first decision tree (BBFT) ensemble and a dagging best-first decision tree (DBFT) ensemble to spatially predict flood probability. Twelve conditioning-factor measures for 272 locations of past floods were used to train and test three models. Receiver operating characteristic (ROC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), kappa (K), and root mean square error (RMSE) are applied to compare and validate the models. The results are that all three models performed well in mapping, flood probabilities (AUC > 0.904). The BBFT model was best, however, with an AUC = 0.96. Based on the results of the Relief-F attribute evaluation method, two soil and slope factors were weighted highest among the parameters, indicating that they are the most important flood-conditioning factors. These models may improve identification of zones that are most susceptible to flooding, improving the capacity for risk management and providing more detailed information for managers and decision-makers.

开发分区和洪水预报模型对于在洪水前后做出最佳管理决策至关重要。伊朗Markazi省的Komijan流域经常受到洪水的影响，洪水造成了巨大的物质损失和生命损失。这项研究的主要目的是使用一种新的机器学习方法来创建三个模型：最佳第一决策树（BFT），装袋最佳第一决策树（BBFT）集合和缓慢的最佳第一决策树（DBFT） ）可以在空间上预测洪水概率。对过去洪水的272个地点的十二个条件因子测量值用于训练和测试三个模型。接收器工作特征（ROC），阳性预测值（PPV），阴性预测值（NPV），灵敏度（SST），特异性（SPF），准确性（ACC），kappa（K），和均方根误差（RMSE）用于比较和验证模型。结果是，所有三个模型在制图，洪水概率（AUC> 0.904）方面均表现良好。BBFT模型最好，但是AUC = 0.96。根据Relief-F属性评估方法的结果，两个土壤和坡度因子在这些参数中的权重最高，表明它们是最重要的洪水调节因子。这些模型可以改善对最容易发生洪灾的区域的识别，提高风险管理的能力，并为管理人员和决策者提供更多详细信息。根据Relief-F属性评估方法的结果，两个土壤和坡度因子在这些参数中的权重最高，表明它们是最重要的洪水调节因子。这些模型可以改善对最容易发生洪灾的区域的识别，提高风险管理的能力，并为管理人员和决策者提供更详细的信息。根据Relief-F属性评估方法的结果，两个土壤和坡度因子在这些参数中的权重最高，表明它们是最重要的洪水调节因子。这些模型可以改善对最容易发生洪灾的区域的识别，提高风险管理的能力，并为管理人员和决策者提供更多详细信息。