The role and impact of environmental big data in agricultural disaster management

ABSTRACT In order to improve the effect of agricultural disaster management, this paper uses environmental big data technology to analyse historical agricultural disaster data and analyse the role and impact of this technology in agricultural disaster management. How to efficiently use the effective resources in the meteorological big data to optimise the mining effect is an important issue in the assessment of agrometeorological disasters. Parallel information entropy attribute reduction algorithm can effectively reduce the scale of research data and increase the rate of data preprocessing. The classification assessment and early warning of agrometeorological disasters involve data mining classification prediction technology. Unlike most classification prediction algorithms, KNN classification is particularly suitable for application to meteorological data sets with cross-class domains and can accelerate the classification and prediction speed of big data in the parallel processing architecture. Finally, this paper designs experiments to verify the method proposed in this paper. The results of data analysis show that the model constructed in this paper has a certain effect.