Studies in Informatics and Control – ICI Bucharest
Chinnaiyan RAJESHKANNAN1*, Shanmuga Vadivel KOGILAVANI2
1 Department of Computer Science and Engineering, Suguna College of Engineering,
Coimbatore- 641014, Tamil Nadu, India
rajeshscholar17@gmail.com (*Corresponding author)
2 Department of Computer science and Engineering, Kongu Engineering College,
Erode – 638060, Tamil Nadu, India
kogilavani.sv@gmail.com
Abstract: One of the major issues of the planetary ecosystem is natural disaster prediction, which utilizes the flood prediction approach to a larger extent. Inundation prevention considers the emergency response, post-event damage assessment and flood mitigation. The current reconstructed flood-mapping model is divided into two segments namely the near real span and real span procedure based on the timing information. The inherent conditions of each procedure broadly hamper their application for inundation mapping. This paper considers the South Carolina inundation in Columbia as a previous research work. The proposed research work presents flood reconstruction design by improving near real span normalized water index derived images from the collection of USGS datasets. Real span data is collected from river gauge readings and it is implemented in Social Media (Twitter API). The proposed flood mapping technique is divided into three sections namely the local enhancement section, enhanced water height section and global enhancement section. Here the section synthesis with gauge readings and various normalized water index images (Geoscience information) is presented with a view to reconstructing an advanced macro-scale flood probability condition by means of the Improved Markov Chain Monte Carlo method, which is improved by using the analyzed inundation-related social media. The result matches well with the U.S. Geological Survey flood map and its surveyed high-water marks for flood identification. The results suggest that by enhancing near real span imagery with real span data sources, the proposed flood inundation probability reconstruction model renders a more robust, gradually enhanced flood probability index for emergency responders so that they quickly identify areas in need of urgent attention. The methodology used in this paper could seed a wide range of future flood studies that may lead to a rapid and improved flood situational awareness in a city as well as at a regional level.
Keywords: Geographic data science, Data fusion, Reconstructing flood mapping, Probability index, Social Media, MCMC technique.
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CITE THIS PAPER AS:
Chinnaiyan RAJESHKANNAN, Shanmuga Vadivel KOGILAVANI, Reconstructing Geographical Flood Probability and Analyzed Inundation Flood Mapping on Social Media Implementation, Studies in Informatics and Control, ISSN 1220-1766, vol. 30(1), pp. 29-38, 2021. https://doi.org/10.24846/v30i1y202103