Evidence map›Paper›PMID 32827298›Full record

ArticleEnvironmental science and pollution research international2021

Predicting water quality during urbanization based on a causality-based input variable selection method modified back-propagation neural network.

Xinzi Wang, Kejia Wang, Jiamu Ding, Xinqi Chen, Yi Li, Wenlong Zhang

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2021. 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
1.1field-weighted citation impact, top 24% 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, 19 citations in OpenAlex.

  1. >Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.Environmental science and pollution research international · 2024
    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

6 authors at 1 institution in 1 country.

Xinzi WangKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China.
Kejia WangKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China.
Jiamu DingKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China.
Xinqi ChenKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China.
Yi LiKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China.
Wenlong ZhangKey Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, People's Republic of China. 1223zhangwenlong@163.com.
Hohai University · CN

Funding

National Key R&D Program of China 2019YFC0408301the National Natural Science Foundation of China 51879079the program of innovation and entrepreneurship of college students 201910294024Z
6 · The paper itself

Abstract

Rapid urbanization has been recognized as the primary cause of deteriorating water quality. Thus, it is crucial to take into account urbanization in water quality forecasting. The present study aims at finding the causal relationship between urbanization and water quality, and then predicting water quality based on this causality. For this purpose, nine urbanization indicators and 12 water quality parameters from 2006 to 2018 in Nanjing were collected as urbanization and water quality indices. Correlation and path analyses were firstly used to identify causal relationships between urbanization and water quality indices. Based on these causal relationships, comprehensive water quality indicators and their correlated urbanization parameters were input into a back-propagation neural network (BPNN) to predict water quality. In the improved BPNN, the R

Indexed as

UrbanizationWater QualityChinaEnvironmental MonitoringNeural Networks, ComputerBack-propagation neural networkGray modelPath analysisPrincipal component analysisUrbanizationWater quality

Identifiers

PMID32827298
OpenAlexW3081330944

What OpenQuestion holds

Textmetadata
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.