A method and device for predicting an air quality index. The method comprises the following steps: according to a geographical location to be predicted and a current time, acquiring a corresponding prediction model (S110); acquiring meteorological data of the geographical location to be predicted (S120); and according to the meteorological data, performing operation processing based on the prediction model, and determining an air quality index of the geographical location to be predicted at a prediction time (S130). The prediction of a future air quality index can be finished by means of only a relatively small amount of meteorological information which can be easily acquired, so that the implementation difficulty and an entry threshold of AQI prediction are reduced. In addition, since in the method, corresponding prediction models are established within all the different prediction time intervals with regard to each geographical location, climate characteristics of the local area can be better reflected and a powerful guarantee is provided for determining an AQI with relatively high accuracy.

Method and device for predicting air quality index

The invention provides a multi-prediction task based air quality prediction system. The system comprises a determination unit, a training unit and a prediction unit, wherein the determination unit is configured to determine an adjacent area of a to-be-tested place according to a distance threshold; the training unit is configured to train a prediction model to obtain optimal parameters of the prediction model, and the prediction model is constructed based on multiple prediction tasks and according to spatial features of the adjacent area; and the prediction unit is configured to predict the air quality of the to-be-tested place by utilizing the prediction model with the optimal parameters. The invention furthermore provides a multi-prediction task based air quality prediction method. According to the multi-prediction task based air quality prediction method and system, the relevance among similar tasks is fully utilized and the accuracy of air pollution prediction is improved.

Air quality prediction system and method

The invention provides a method for predicting atmospheric PM2.5 concentration based on a VARX model. The method comprises the following steps of: (1), collecting pollutant concentration data and meteorological data of a target city; (2), reading the pollutant data and the meteorological data of the target city from a database; (3), performing principal component analysis of the data, except PM2.5; (4), performing a root test of a data set; (5), constructing a VARX equation model; (6), determining a lagged order of the model by using an AIC Akaike information criterion and a SC Schwarz criterion; and (7), predicting the PM2.5 concentration by correcting and using the VARX model. The method disclosed by the invention has the characteristics of being relatively high in generalization ability, relatively high in prediction precision and the like.

Method for predicting atmospheric PM2.5 concentration based on VARX model

The present invention discloses a small-scale air quality index prediction method and system for a city. The method comprises: firstly dividing a city area into a grid of multiple locations to undergo prediction; acquiring historical data related to each model and creating the following models on the basis of the historical data: a time prediction model respectively corresponding to a prediction relating to a current moment and predictions relating to each moment in a future period of time, a spatial prediction model for performing air quality predictions with respect to locations at specified coordinates, a dynamic prediction model for characterizing a relationship of traffic data and data of a geographical location of interest to an air quality index, and an indoor and outdoor prediction model for characterizing a relationship of an indoor air quality index to an outdoor air quality index; when performing prediction, performing coordinated training on the created time prediction model, the spatial prediction model, the dynamic prediction model and the indoor and outdoor prediction model with respect to any one of the locations to undergo prediction at any real-time moment, so as to merge prediction results of all of the models and obtain air quality index predicted values of each of the locations to undergo prediction at a corresponding current moment, and at each moment in the future period of time.

Small-scale air quality index prediction method and system for city

The invention discloses a method for predicting the PM2.5 concentration of regional air. The method comprises the steps that firstly, training sample data of a support vector machine regression model to be trained are constructed through historical data, then the trained support vector machine regression model is obtained through the training sample data, and the trained support vector machine regression model is treated as a PM2.5 concentration prediction model; then a particle swarm optimization algorithm is combined with the PM2.5 concentration prediction model, through the continuous optimization and iteration of the particle swarm optimization algorithm, input parameters of the PM2.5 concentration prediction model are reconstructed continuously through the particle positions till the final global polarity of a particle swarm is obtained after iteration is completed, an input parameter of the PM2.5 concentration prediction model is reconstructed with the position of the particle corresponding to the final global extreme value of the particle swarm, and when the input parameter is input into the PM2.5 concentration prediction model, the obtained output is considered as the PM2.5 concentration. The method has the advantages that the dimensionality of the input parameters of the PM2.5 concentration prediction model can be lowered, and the PM2.5 concentration prediction accuracy can be improved.

Method for predicting PM2.5 concentration of regional air

The invention relates to a PM2.5 concentration prediction method based on feature vectors and a least square support vector machine, and belongs to the field of environment pollution prediction. The method includes the steps that firstly, pollutant concentration data relevant to the PM2.5 concentration are collected and preprocessed; then comprehensive weather indexes are calculated; correlation analysis is conducted on the pollutant concentration data relevant to the PM2.5 concentration and the comprehensive weather indexes, and then feature vectors containing the comprehensive weather indexes are acquired to form feature vectors A and feature vectors in which the comprehensive weather indexes are removed are acquired to form feature vectors B; eventually, a training sample training LS-SVM model is formed through the feature vectors A and the feature vectors B, and the prediction result is evaluated. By the combination of environment monitoring data and the actual situation, air humidity, wind power and humidity are combined with a formation mechanism of PM2.5, and the new concept of a comprehensive weather index formula is provided; prediction accuracy is high.

PM2.5 concentration prediction method based on feature vectors and least square support vector machine

Objective: Systematically review the value of second-trimester triple serum tests screening for Down's syndrome by using meta analysis. Method: The literature on PubMed, Journal of Medicine Library, CNKI, Web of Knowledge from January 2000 to April 2014 concerning second-trimester Down's syndrome screening was retrieved by computer or by hand. The literature was evaluated based on the QUADAS quality evaluation standard. The basic fourfold table data were extracted from the literature. Statistical analysis was conducted by using SPSS and Meta-Disc1.4. Results: Altogether 166 related articles were retrieved. According to the standard of literature selection and QUADAS quality evaluation, eventually 14 articles were brought into research. The meta analysis results of second-trimester triple serum tests screening for Down's syndrome were as follows: the sensitivity was 77%, the specificity was 93%, + LR was 13.92,-LR was 0.26, DOR was 40.82, the AUC of SROC was 0.9133, and Q∗= 0.8458. In addition, the results of subgroup analyses based on age showed that the pooled sensitivity of high age group is higher than low age group, which indicated that the second-trimester serum triple serum tests screening for Down's syndrome could achieve better results for advanced maternal age. Conclusion: The second-trimester triple serum tests screening for Down's syndrome had a certain sensitivity and specificity, but for the regions with good medical and health care, they could consider increasing the serum screening indexes or adding ultrasound screening method so as to further improve the detection rate of Down's syndrome children and reduce the false-positive rate.

Second-trimester triple serum tests in women screening for down's syndrome: A meta analysis

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PM2.5 concentration prediction method based on feature vectors and least square support vector machine

Second-trimester triple serum tests in women screening for down's syndrome: A meta analysis