Ash haze predicting system and method based on BP neural network

Abstract

The invention provides an ash haze predicting system and method based on a BP neural network. The system excavates a relation among collected air quality data by means of the BP neural network, predicts air quality and ash haze weather development tendency, and provides early warning for the ash haze weather, and is characterized by comprising a data acquisition module, a database, a data arranging module, an ash haze prediction server, a data preprocessing module, a neuron learning module, a BP neural network prediction module, a WEB server and a mobile terminal. The method comprises steps of: describing the ash haze predicting system as a MIMO self-learning prediction system, inputting acquired ash haze and air quality data, predicting possible development tendency of ash haze weather by means of the self-learning and adaptive capability of the neural network, and reducing a prediction error. The ash haze predicting system and method may predict the ash haze development by using existing ash haze observations, deeply excavates a complex relation among input data, and obtain an accurate prediction result.

Description

technical field [0001] The invention relates to a haze prediction method and technology, in particular to a haze prediction system and method based on a BP neural network. Background technique [0002] At present, the haze phenomenon in China has attracted more and more attention, and researches on haze prediction analysis and governance strategies have emerged in an endless stream. implementation etc. The current haze prediction methods and systems mainly focus on the monitoring of ecological environment and air quality indicators for modeling, and use the monitored indicators such as PM2.5 and PM1.0 to try to accurately describe the mathematical model and evolution mechanism of haze. The latest technology also includes the deployment of sensor networks in haze-prone areas and satellite image analysis technologies, which provide a large amount of accurate data for haze monitoring and early warning, and then use this as a basis to analyze and predict the development trend o...

AI Technical Summary

Problems solved by technology

Moreover, the natural environment and social environment where the haze is located have a self-learning nature, which is difficult for traditional forecasting methods to take into account

In China, with the application of data processing technology in haze prediction, it provides conditions for the use of data collection and self-learning methods to predict haze. However, there are few haze prediction methods and systems based on BP neural network in the market

Images