Research On Air Pollution Prediction And Prevention Based On Deep Learning

Intelligence analysis is an important part of intelligence work,which is goal-oriented and information-based.Data is analysed through technical means to obtain new knowledge and the knowledge obtained is generalised,correlated,deducted,reasoned and integrated to form new and more valuable information.With the advent of the big data era,data is becoming more and more massive and fragmented,and traditional intelligence analysis methods are no longer able to fully tap and utilise multiple sources,multiple dimensions and massive amounts of data,while with the development of artificial intelligence,the application of deep learning and big data analysis technology is becoming increasingly widespread,providing full process technical support for intelligence services in terms of information collection,storage,analysis and even decision-making.This paper examines the prevention and control of air pollution based on the prediction of air pollutant concentrations,as well as the application of deep learning technology in intelligence analysis work.With General Secretary Xi Jinping’s goal of building an ecological civilisation where "green water and green hills are golden mountains",society’s awareness of environmental pollution prevention and control is constantly rising.Air pollution is one of the most important and relevant issues for everyone.In order to propose effective measures to prevent and control air pollution,accurate prediction of the trend of air pollutant concentrations is a key prerequisite.In this paper,based on the research on air pollution prediction methods and air pollution control strategies at home and abroad,a long short-term memory(LSTM)neural network prediction model was built to deal with time series problems,and the attention mechanism was introduced to design and implement the air pollutant prediction model based on the LSTM.The air pollutant prediction model based on the attention mechanism LSTM was designed and implemented,and then the air pollutant concentration data collected from several major observation points in Taiyuan,Shanxi Province and the related meteorological data were selected for experiments to verify the feasibility and accuracy of the model.The main contents of the thesis include.In this paper,based on the research on air pollution prediction methods and air pollution strategies at home and abroad,a long short-term memory(LSTM)neural network prediction model was built for dealing with time series problems,and the attention mechanism was introduced to improve it,and an air pollutant prediction model based on the attention mechanism was designed and implemented.The model is then used to predict air pollutants based on the attention mechanism LSTM,and then experiments are conducted on the air pollutant concentration data collected from several major observation points in Taiyuan,Shanxi Province,together with relevant meteorological data to verify the feasibility and accuracy of the model.The main contents of the thesis include.(1)Learn about the application of deep learning techniques in the field of intelligence work,the development of air pollution prediction methods at home and abroad,as well as understanding the domestic and international strategies for air pollution prevention and control,and to conduct a review of research developments from various perspectives and thematic aspects in order to identify prediction models.(2)The data is pre-processed with historical pollutant data collected from observation points and meteorological monitoring data to build an LSTM-based air pollution prediction model and introduce an attention mechanism to improve the performance of the model.(3)Based on the concentration limits of the six major pollutants in different regions according to the national air standards,corresponding air pollution prevention and control measures are formulated based on the prediction of the trend of pollutant concentration changes.The research work in this paper is supported by deep learning technology and focuses on air pollutant management,which is the core problem in environmental management.The LSTM model with attention mechanism is constructed to predict air pollutant concentrations and propose prevention and control measures based on the prediction results.The results of this paper will enrich the theoretical research on air pollutant management,improve the efficiency of air pollution management,and provide reference for relevant departments to carry out air pollutant prevention and control work.