Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework

Abstract

The era of big data, analysis, and artificial intelligence is a new trend in intelligent big data analysis. The present stage of the geoscience progress allows the Earth to be analyzed as an extremely dynamic structure of multiple elements, such as the hydrosphere, lithosphere, and atmosphere, interacting in and with others. To derive useful information from them, large quantities of observation and simulation data provided by numeric models need to be analyzed. Visualization is a critical feature of data analytics since it is a simple and swift way to evaluate the data and consider the specific aspects and mistakes of the dataset. A geographic information system (GIS), the most efficient meteorological data visualization software class, provides excellent capabilities for geospatial data manipulation. The processing architecture that can efficiently be used as a back-end for GIS by providing quick access to the data stored at remote storage nodes is described in this paper. Weather departments use various kinds of sensors for data collection such as temperature, humidity, etc. The number and speed of the sensors in each sensor complicate the data processing time. This paper seeks to provide a weather-temperature analysis big data forecast architecture based on the MapReduce algorithm. The suggested intelligent management system of meteorological archives based on big data (IMS-MABD) framework methodology could promote research and advancement of intelligent big data analysis, large data analytics, business intelligence, artificial intelligence, and data science. Intelligent management systems for meteorological archive systems based on large data frameworks could be used. Experimental findings show that the architecture created allows real-time data access and can support many simultaneous applications successfully with a performance of 98.1%.