Open AccessDesign of Risk Early Warning Model Based on Big Data
Author(s) -
Shen Qi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012172
Subject(s) - pollutant , environmental science , pollution , chemical oxygen demand , sulfur dioxide , nitrogen oxide , environmental engineering , nox , chemistry , inorganic chemistry , ecology , organic chemistry , wastewater , biology , combustion
With the development of China’s economy, pollution has become more and more serious, so total pollution control has become a priority for governments at all levels. The center of total pollutant emission reduction is to formulate scientific and reasonable pollutant emission reduction policies based on the forecast of pollutant emission in the next few years. According to the national requirements, the emissions of four pollutants, namely sulfur dioxide, nitrogen oxide, ammonia nitrogen and chemical oxygen demand, need to be counted. The research objective of this paper is to establish a combined model applicable to the emission prediction of sulfur dioxide, nitrogen oxide, ammonia nitrogen and chemical oxygen demand by analyzing the impact of population factors and industrial distribution on pollutant emissions and combining the grey system theory and neural network theory. According to the characteristics of pollutant discharge in a certain city, a pollutant discharge prediction model based on GM (1,1), GM (0,4) and BP neural network is proposed.