Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.

地震等灾难性自然灾害严重威胁着城市地区的财产和生命。因此，地震风险评估（ERA）在灾难管理中是必不可少的。ERA是资产概率和脆弱性范围的综合。本研究通过使用人工神经网络-层次分析法（ANN-AHP）模型构建ERA映射图，开发了一个集成模型。该研究的目的是量化可能由地震引起的城市人口风险。该模型应用于印度尼西亚班达亚齐市，这是亚齐省的地震活跃区，经常遭受破坏性地震的影响。ANN用于概率映射，而AHP用于在借助地震烈度变化专题分层创建危害图之后评估城市脆弱性。随后通过组合概率图，危害图和脆弱性图来创建风险图。然后，获得各个区域的风险等级。验证过程表明，所提出的模型可以根据历史事件绘制地震概率图，准确度为84％。此外，结果表明，该城市的中部和东南部地区具有中等到非常高的风险分类，而该城市的其他地区则属于低到非常低的地震风险分类。这项研究的结果对政府机构和决策者很有帮助，