Assam is one of the most flood-prone states in India, and the state frequently experiences catastrophic floods that cause significant damage in terms of loss of life and property. Flood susceptibility is considered the most essential and crucial input for managing floodplains and fostering local and regional development. This study focuses on the generation of flood susceptibility maps using the Frequency Ratio (FR) technique and microwave remote sensing inputs in the Jinjiram watershed which experienced disastrous flooding in 2020. The study has been carried out by taking into consideration of different morphological, lithological, and hydrological factors. In this study the flood inventory map was created by extracting the time series SAR (Synthetic Aperture Radar) Sentinel 1 GRD (Ground Range Detected) images of flooded areas for the past 5 years, from 2016 to 2020. A total of 72 inventory samples were identified of which 70% of total flooded samples were chosen for training and 30% for model testing at random basis. Applying these FR methods, the study determines a range of flood susceptibility which was then divided into five classes, from very low to very high. The Receiver Operating Characteristics (ROC) analysis was used to evaluate the accuracy of flood susceptibility maps generated using FR models. The AUC of ROC in flood susceptibility mapping is 0.81167 achieved, corresponding to a prediction accuracy of 81.17%. The findings can be used to calculate risk, develop flood control measures and infrastructural policies, and formulate sustainable water management policies for the watershed.

阿萨姆邦是印度最容易发生洪水的邦之一，该邦经常发生特大洪水，造成重大人员伤亡和财产损失。洪水敏感性被认为是管理洪泛区和促进地方和区域发展的最重要和最关键的投入。本研究的重点是利用频率比 (FR) 技术和微波遥感输入在 2020 年经历了灾难性洪水的金吉拉姆流域生成洪水敏感性图。该研究考虑了不同的形态、岩性、和水文因素。在本研究中，通过提取过去 5 年（2016 年至 2020 年）洪水地区的时间序列 SAR（合成孔径雷达）Sentinel 1 GRD（地面范围检测）图像来创建洪水清单地图。总共有 72 个清单样本确定其中随机选择总淹没样本的 70% 用于训练，30% 用于模型测试。应用这些 FR 方法，该研究确定了洪水敏感性范围，然后将其分为五个等级，从非常低到非常高。接收器操作特性 (ROC) 分析用于评估使用 FR 模型生成的洪水敏感性图的准确性。洪水敏感性测绘中ROC的AUC为0.81167，对应的预测精度为81.17%。研究结果可用于计算风险、制定防洪措施和基础设施政策，以及制定流域的可持续水管理政策。