Abstract

Earthquake hazards cause changes in landforms, economic losses, and human casualties. Seismic Vulnerability Mapping (SVM) is key information to prevent and predict the damage of earthquakes. The purpose of this study is to train and compare the results of the Classification Tree Analysis (CTA) learner model with three Gini, Entropy, Ratio split algorithms, and Fuzzy ARTMAP (FAM) model by the development of hybrid models for SVM. The Seismic Vulnerability Conditioning Factors (SVCFs) such as environmental, physical, and social were selected using experts' opinions and experience. Thirteen factors were edited and prepared as the layers used in this study. In order to seismic vulnerability mapping and model training, a database of training sites was created by the Multi-eriteria Decision Analysis-Multi-Criteria Evaluation (MCDA-MCE) hybrid process. Then, 70% of the points were used for training and 30% were used to validate the models' results based on the holdout method. Moreover, Relative Operating Characteristics (ROC), Seismic Relative Index (SRI), and Frequency Ratio (FR) were used to validate the results. The Area under the curve (AUC) for the algorithms Gini, Entropy, Ratio, and FAM model are 0.895, 0.890, 0.876, and 0.783, respectively. The results of the three validation methods show the highest performance for the Gini splitting algorithm. Accordingly, the percentage of social and physical vulnerability of Sanandaj city was determined based on the MCE-Gini optimal model: 27% of the area and 62% of the population of Sanandaj are under high vulnerability to earthquakes. So that, various factors such as worn urban texture, high population density and environmental factors were among the most important factors affecting seismic vulnerability.

摘要

地震危害导致地貌的变化，经济损失和人员伤亡。地震易损性映射（SVM）是预防和预测地震破坏的关键信息。这项研究的目的是通过开发支持向量机的混合模型，训练和比较分类树分析（CTA）学习器模型与三种基尼，熵，比率分割算法和模糊ARTMAP（FAM）模型的结果。使用专家的意见和经验选择了诸如环境，自然和社会等地震脆弱性调节因素（SVCF）。编辑并准备了十三项因素作为本研究中使用的层。为了进行地震脆弱性制图和模型训练，通过多标准决策分析-多标准评估（MCDA-MCE）混合过程创建了一个培训站点数据库。然后，根据保持方法，将70％的点用于训练，并使用30％的点对模型的结果进行验证。此外，使用相对运行特性（ROC），抗震相对指数（SRI）和频率比（FR）来验证结果。算法Gini，熵，比率和FAM模型的曲线下面积（AUC）分别为0.895、0.890、0.876和0.783。三种验证方法的结果显示了Gini分割算法的最高性能。因此，根据MCE-Gini最佳模型确定了Sanandaj市的社会和生理脆弱性百分比：Sanandaj的27％的地区和62％的人口非常容易受到地震的伤害。因此，各种因素，例如磨损的城市质地，高人口密度和环境因素是影响地震脆弱性的最重要因素。