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Utilizing Geo-Social Media as a Proxy Data for Enhanced Flood Monitoring
Journal of the Indian Society of Remote Sensing ( IF 2.5 ) Pub Date : 2021-06-07 , DOI: 10.1007/s12524-021-01376-9
Jaya Surya Sattaru , C. M. Bhatt , Sameer Saran

Social media plays an important role in disseminating spontaneous information during natural disasters/emergencies. It is a crowdsourcing platform, capable of complementing and supplementing remote sensing data in disaster mapping. Continuous monitoring of disasters such as floods from pre-stage to post stage is essential and geo-social media can attain it. The present research proposes a robust architecture to develop a social media-based near-real time flood monitoring system. In addition, our research article also emphasizes the efficient methods to process, analyse and explore multiple data dimensions of social media. A prototype model was prepared and tested on the tweets of Chennai floods 2015 to demonstrate social media potential in disaster monitoring. We implemented Natural Language Processing and Supervised Machine Learning in data processing and analysis segments of the framework by assembling various open source python libraries to develop the prototype. Initially, we built the required tweet corpus and performed pre-processing steps on it. Later, the collected tweets were geocoded with the place names available in the tweets and classified them into various flood topic related classes using Naive Bayes classifier. Subsequently, the tweets showcasing the flood condition were determined to generate a point map and a point density map to identify the flood hotspots. We verified our results with openly available 2015 Chennai flood map that is generated using remote sensing images and found positive outcome. A prototype web portal was developed to publish the results from above model as web maps. Furthermore, the portal would prove to be useful as a source for disseminating information to the public. The results prove that the proposed framework is evidently supportive in establishing a near real time monitoring system during emergencies.



中文翻译:

利用地理社交媒体作为增强洪水监测的代理数据

社交媒体在自然灾害/紧急情况期间传播自发信息方面发挥着重要作用。它是一个众包平台,能够补充和补充灾害测绘中的遥感数据。从前期到后期对洪水等灾害的持续监测至关重要,地理社交媒体可以实现这一点。本研究提出了一种强大的架构来开发基于社交媒体的近实时洪水监测系统。此外,我们的研究文章还强调了处理、分析和探索社交媒体多个数据维度的有效方法。在 2015 年钦奈洪水的推文上准备并测试了一个原型模型,以展示社交媒体在灾害监测中的潜力。我们通过组装各种开源 Python 库来开发原型,在框架的数据处理和分析部分实现了自然语言处理和监督机器学习。最初,我们构建了所需的推文语料库并对其执行了预处理步骤。后来,收集到的推文使用推文中可用的地名进行地理编码,并使用朴素贝叶斯分类器将它们分类为各种与洪水主题相关的类。随后,确定展示洪水状况的推文以生成点图和点密度图,以识别洪水热点。我们使用使用遥感图像生成的公开可用的 2015 年钦奈洪水地图验证了我们的结果,并发现了积极的结果。开发了一个原型网络门户,以将上述模型的结果发布为网络地图。此外,该门户网站将被证明是向公众传播信息的有用来源。结果证明,所提出的框架明显支持在紧急情况下建立近乎实时的监控系统。

更新日期:2021-06-07
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