Mine water inflow prediction model based on variational mode decomposition and gated recurrent units optimized by improved chimp optimization algorithm.

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

–field-weighted citation impact

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.

VMD-LSTM based water level prediction of aquifer in mining working face.Scientific reports · 2026
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

2 authors.

Juntao ChenCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.ORCID https://orcid.org/0000-0002-7507-0981

Mingjin FanCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, 266590, China. 15216335378@163.com.ORCID https://orcid.org/0009-0002-7691-1786

Funding

National Natural Science Foundation of China 52104203Natural Science Foundation of Shandong Province ZR2022ME140Project of State Key Laboratory of Efficient Mining and Clean Utilization of Coal Resources 2021-CMCU-KF015

6 · The paper itself

Abstract

Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R

Indexed as

Gated recurrent unitsImproved chimp optimization algorithmVariational mode decompositionWater damage accidents

Identifiers

PMID39910099

DOI10.1038/s41598-024-82580-8

PMCPMC11799420

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.