Recurrent floods are severely affecting the built assets and people of numerous Indian cities. Urban flood being a comparatively nascent area of research is often dealt with strategies apt for other disasters. The conventional approach of hazard zonation lacks vital information on human vulnerability. This paper addresses this lacuna by identifying vulnerable population and their precise cause of vulnerability for a case study of the city of Guwahati. It is the largest urban and financial centre of north-east India, a disaster hotspot and exclusive link connecting the region and the mainland. Guwahati's 31 municipal wards were gradedparallel for flood hazard and human vulnerability in five categories each (very high to very low). Analytic Hierarchy Process based survey with 16 domain experts and Geographic Information System were used for hazard mapping using factors causing urban flood grouped under environment and urbanization. For vulnerability mapping, questionnaire survey, based on the Human Development Index and other published disaster vulnerability indices, was carried out with 1023 citizens. For 38.70% cases, wards descended by one category from hazard grouping to vulnerability grouping i.e. a ward prone to flooding may not be perceived as equally vulnerable. A strong correlation of 73.5% validated this fact. For obtaining a holistic picture, the top-down approach from experts was tallied with its bottom-up counterpart of citizen's observation. The knowledge will help in making focused policies and prioritize funds for development planning, that are critical for cities of developing countries which lack resources to tackle the growing wrath of urban floods.

反复发生的洪灾严重影响了许多印度城市的建筑资产和人民。城市洪水是一个相对较新的研究领域，通常要采用适合于其他灾害的策略。传统的危险区划方法缺乏有关人类脆弱性的重要信息。本文针对古瓦哈提市的案例研究，通过识别弱势群体及其脆弱性的确切原因，解决了这一缺陷。它是印度东北部最大的城市和金融中心，是灾难热点，是连接该地区与大陆的独家链接。Guwahati的31个市级病房在洪水危害和人身伤害方面分为五个等级（从高到低）。使用由16位领域专家和地理信息系统进行的基于层次分析法的调查，使用环境和城市化条件下导致城市洪灾的因素对灾害进行了制图。为了绘制脆弱性图，根据人类发展指数和其他已发布的灾害脆弱性指数，对1023名公民进行了问卷调查。对于38.70％的案例，病房从危险分组到脆弱性分组仅属于一类，即容易发生洪水的病房可能不会被视为同等脆弱。73.5％的强相关性验证了这一事实。为了获得整体图景，将专家的自上而下方法与公民观察的自下而上方法相吻合。这些知识将有助于制定针对性的政策，并优先安排用于发展计划的资金，