Evidence map›Paper›PMID 36505938›Full record

ArticleiScience2022

Prediction of ground-level ozone by SOM-NARX hybrid neural network based on the correlation of predictors.

Qinqing Xiong, Wenju Wang, Mingya Wang, Chunhui Zhang, Xuechun Zhang, Chun Chen, Mingshi Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Qinqing XiongCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Wenju WangCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Mingya WangCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Chunhui ZhangCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Xuechun ZhangCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Chun ChenHenan Key Laboratory for Environmental Monitoring Technology, Zhengzhou 450004, China.
Mingshi WangCollege of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Henan Polytechnic University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current approaches to ozone prediction using hybrid neural networks are numerous but not perfect. Decomposition algorithms ignore the correlation between predictors and ozone, and feature extraction methods rarely select appropriate predictors in terms of correlation, especially for VOCs. Therefore, this study proposes a hybrid neural network model SOM-NARX based on the correlation of predictors. The model is based on MIC to filter predictors, using SOM to make predictors as feature sequences and using NARX networks to make predictions. Data from the JCDZURI site were used for training, testing, and validation. The results show that the correlation of the predictors, classification numbers of SOM, neuron numbers, and delay steps can affect prediction accuracy. Model comparison shows that the SOM-NARX model has 13.82, 10.60, 6.58% and 12.05, 9.44, 68.14% RMSE, MAE, and MAEP in winter and summer, which is smaller than CNN-LSTM, CNN-BiLSTM, CNN-GRU, SOM-LSTM, SOM-BiLSTM, and SOM-GRU.

Indexed as

Atmospheric scienceEnvironmental monitoringRemote sensing

Identifiers

PMID36505938
PMCPMC9732375
OpenAlexW4309927914

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.