Unlock instant, AI-driven research and patent intelligence for your innovation.

Combining ssae deep feature learning and lstm's pm 2.5 Hourly Concentration Prediction Method and System

A deep feature and concentration prediction technology, applied in neural learning methods, prediction, data processing applications, etc., can solve the problems of considering the influencing factors comprehensively, ignoring the lack of spatial correlation of PM2.5, and difficult to select combinations, etc. Improve prediction accuracy and have the effect of generality

Active Publication Date: 2022-05-27
FUZHOU UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the non-mechanistic model, the long-short term memory (LSTM) neural network with temporal memory is used in PM 2.5 Some results have been achieved in prediction, but there are still two problems: 1) Some existing methods only consider the influence of meteorological factors on PM 2.5 or only consider the impact of air pollutants on PM 2.5 effect, ignoring the PM 2.5 spatial correlation without PM 2.5 2) Existing methods select PM from a large number of influencing characteristics according to certain evaluation criteria or empirical knowledge. 2.5 Features with important influence, it is difficult to select the optimal combination

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Combining ssae deep feature learning and lstm's pm <sub>2.5</sub> Hourly Concentration Prediction Method and System
  • Combining ssae deep feature learning and lstm's pm <sub>2.5</sub> Hourly Concentration Prediction Method and System
  • Combining ssae deep feature learning and lstm's pm <sub>2.5</sub> Hourly Concentration Prediction Method and System

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0032]It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and / o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a PM that integrates SSAE deep feature learning and LSTM network 2.5 Hourly concentration prediction method and system, constructing the SSAE-LSTM model, inputting the time series of air pollutants with a certain time step into the model, using the SSAE network to extract the abstract features of the input data through an unsupervised method, and using the extracted features as the LSTM network The input features of the input feature, obtain the characteristic distribution of air pollutant information within a certain time step, and finally combine the fully connected network to predict PM 2.5 hour concentration. The invention can effectively improve the prediction accuracy.

Description

technical field [0001] The present invention relates to PM 2.5 The field of prediction, especially a PM that fuses SSAE deep feature learning and LSTM networks 2.5 Hourly concentration prediction method and system. Background technique [0002] In recent years, with PM 2.5 The haze pollution, which is an important component, is highly frequent, bringing serious harm to human life and the ecological environment. Considering the timeliness and dynamics of the atmospheric environment to achieve accurate PM 2.5 Hourly concentration prediction can effectively improve the forecasting and early warning ability of air pollution, and it is also an important research direction of current air quality forecasting and prevention. [0003] At present, air pollutant concentration prediction models are roughly divided into two types: mechanism models and non-mechanism models. Among them, the mechanism model predicts the concentration of pollutants by simulating the diffusion process of...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06Q50/26
CPCG06Q10/04G06Q50/26G06N3/084G06N3/044G06N3/045
Inventor 邬群勇邓丽
Owner FUZHOU UNIV