Numerous disasters impact people around the world, including floods, cyclones, tsunamis and so on. Disaster resilience (recovery) is a realtime challenge. The research goal of the proposed Natural Disaster Resilience Approach (NDRA) is to fasten and automate disaster resilience within the affected region. Prioritization for saving lives must be provided to the region most affected. Currently almost all people use the Online Social Networks (OSNs) to share their daily activities. Actually only their friends in OSN can note the photos posted by OSN users in the disaster-affected region and in turn can support the friends in need. Our research goal is to conduct speedier and prioritized disaster resilience processes by directly addressing honest disaster help requests from images posted by OSN users. To achieve the above goal, we suggest an approach known as the Natural Disaster Resilience Approach (NDRA). The NDRA is a three-tiered framework: Tier-1: sybil(malicious) user avoidance engine: prevents fake OSN user profile creation; Tier-2: ‘D’- attributed image classifier: determines the originality of the images posted and Tier-3: sybil user prediction: uses an Advanced Sybil Node Prediction Algorithm (ASYNPA) to check the authenticity of the user and priorities for faster resilience requests. In NDRA we use Advogato dataset with 6541 users and 51,127 edges. Ultimately, the comparison is only made between the proposed ASYNPA tier-3 and the existing VoteTrust algorithm, and the graph is plotted against the False Positive (FP) rate, taking into account the precision and recall metrics. Around 99.84% of the expected sybils were confirmed in ASYNPA, which is 3.49% higher than VoteTrust.

无数的灾难影响着世界各地的人们，包括洪水，飓风，海啸等。灾难复原力（恢复）是一项实时挑战。拟议的“自然灾害抗灾力方法”（NDRA）的研究目标是在受灾区域内提高灾害抗灾力并使其自动化。必须向受影响最严重的地区提供拯救生命的优先次序。当前，几乎所有人都使用在线社交网络（OSN）共享他们的日常活动。实际上，只有他们在OSN中的朋友才能记下OSN用户在受灾地区发布的照片​​，从而可以为有需要的朋友提供支持。我们的研究目标是通过直接解决OSN用户发布的图像中真实的灾难帮助请求，来进行更快且优先级更高的灾难恢复过程。为了达到上述目标，我们建议采用一种称为自然灾害复原力方法（NDRA）的方法。NDRA是一个三层框架：Tier-1：sybil（恶意）用户回避引擎：防止创建虚假的OSN用户配置文件；第2层：“ D”-归因于图像分类器：确定所发布图像的原始性；第3层：sybil用户预测：使用高级Sybil节点预测算法（ASYNPA）来检查用户的真实性和优先级，以提高弹性要求。在NDRA中，我们使用具有6541个用户和51,127条边的Advogato数据集。最终，仅在拟议的ASYNPA tier-3与现有的VoteTrust算法之间进行比较，并根据误报率（FP）绘制图表，同时考虑精度和召回率指标。在ASYNPA中确认了约99.84％的预期西比尔，即3。