LAUSR

The disaster management contains collections of real-time natural disaster information, expositions, sets, analyses, forecasts and illustration. It is observed that progression of information knowledge in the form of Geographic Information System (GIS). The disaster management method for natural events, include with GIS and spatial data mining and it can recognize the natural events location and the optimal routes are provided to attain to the desired location without harmful. Due to the exacting geological condition and geographical location, numerous locations damaged from various natural events such as earthquake, flooding, land debris, landslides, earthquakes and cloud burst that can frequently reason sequence assets damages and also life losses. To decrease the damages and sufferer, an effectual real-time method for natural events and location prediction is essential. Therefore, in this paper, a novel framework is presented for discovering the disaster location and event prediction employing the density-based spatiotemporal clustering with GPS. In this process, the noisy data, unwanted and inconsistent data is cleansed from the news database based on natural events to generate the structured data before the implementation of clustering and feature selection. The spatiotemporal clustering method will extract disaster areas such as area of earthquake, flood, landslide and etc. Subsequently, feature is chosen depending on the natural disasters keywords from the clustered data. Extracted feature is given to the decision tree to divide the data into positive and negative class for the assists of event detector and location estimator. The prediction is improved by utilizing the Genetic Algorithm (GA). Hence, GPS technology is the significant data cause of geographic or earth information scheme for monitoring alter of global. We exploit GPS as a location estimator to discover the location of disaster occurred. Therefore, the location of natural disasters can be estimated and predicted using the GPS.