Outliers Analysis of Real-time Metereological data using Elastic Search repositories for a proposed Natural Disaster Prediction System (NDPS)

Abstract: Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using scientific understanding of atmospheric processes to project how the atmosphere will change. But in order to make quick and exact forecasting decisions we will need to have a critical observation points to give alerts about the fluctuations in the weather conditions. Quick prediction of natural calamities or other environment fluctuations on a real time data can be implemented with the help of using and analyzing Outliers within the data inside elastic search repositories. Our proposed system will consist of five major components: 1) Weather data collecting agent: Weather live feed/Historic data. 2) Processing agent: Coverts the data in JSON format for storing in elastic search repository. 3) Repository: Using Elastic Search technology. 4) Outlier Analyzer: Contains the logic to perform analysis of the disaster prediction. To improve the accuracy of prediction the outlier rules have been mapped to a Bayesian network model. 5) Display agent: Provides a display in the form of alerts, alarms or graphical for warnings based on what outlier was activated.

Keywords: Weather Forcasting,Outlier,ElasticSearch,JSON,Bayesian Network,Natural Calamity.