Design And Implementation Of Haze Spatio-temporal Distribution System Based On Deep Learning

At present,due to heavy air pollution caused by heavy industry and environmental pollution,a new type of weather "haze" is generated.As humans do not restrict their own behavior,the evolution of climate conditions is becoming more and more serious."Haze" became a hot issue that people were concerned about.More and more people are aware of the dangers of smog.People are more eager to understand the prediction of the probability of haze in the future and how to deal with it if serious pollution occurs.The current evaluation standard for air quality is the Air Quality Index(AQI),and the value of AQI is used as the evaluation standard for judging the pollution level of smog.The prediction of the future AQI value is our need for research on smog.This article analyzes the spatial and temporal distribution and causes of smog in Beijing-Tianjin-Hebei area based on geospatial information.Based on the experimental results,relevant government departments can formulate accurate and targeted governance measures to reduce direct and indirect losses caused by smog.Residents can also use this to arrange daily activities.This paper designs and implements a "deep space-time distribution system based on deep learning",and does the following:(1)The main influencing factors of smog in Beijing-Tianjin-Hebei region are mapped with geographic information system software Arc GIS,and the spatial and temporal distribution characteristics of smog in Beijing-Tianjin-Hebei region are analyzed based on the visualization results.(2)Time series prediction of AQI valueThe deep neural network haze prediction model can extract the inherent characteristics of the data,has a strong ability to process non-linear data,has good prediction capabilities for haze data with time series characteristics,and can extract deep features to predict haze weather.the goal of.(3)Design and implementation of the spatio-temporal distribution system of smog in the Beijing Tianjin-Hebei regionThe system includes three main modules: regional distribution module,data visualization module,and AQI numerical prediction module.Among them,the regional distribution module uses Arc GIS software and geographic information to make maps to help study the spatial distribution of smog in Beijing-Tianjin-Hebei region from 2014 to 2017.The data visualization module makes spatial statistical analysis of the smog data of the cities in Beijing-Tianjin-Hebei area from 2014 to 2017,and visually displays the analysis results.The AQI numerical prediction module uses the trained deep neural network to establish a haze prediction model to predict the AQI value,predict the AQI value,air quality level and pollution level of each city in the Beijing-Tianjin-Hebei region in the next three days and write it into the back-end database,To facilitate the call of the system front end.In this paper,a haze prediction system is established based on deep neural networks.The cause and temporal and spatial distribution of haze are visually displayed and analyzed.The haze prediction system is an effective supplement to the research on the haze space-time distribution system and haze prediction in China.Use this as a basis for arranging daily activities.