Deep learning-based intelligent prediction of strata pressure in a steeply inclined fully mechanized longwall face.

Zhuocheng Ding, Naizhong Xu, Chang Su

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

3 authors.

Zhuocheng DingState Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection , Anhui University of Science and Technology, Huainan, 232001, China.

Naizhong XuState Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection , Anhui University of Science and Technology, Huainan, 232001, China. 17775499674@163.com.

Chang SuSchool of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Huainan, 232001, China.

Funding

the National Natural Science Foundation of China Grant No. 52374156

6 · The paper itself

Abstract

Reliable prediction of strata-pressure evolution is essential for intelligent longwall mining, but steeply inclined panels show spatially heterogeneous support loading that challenges short-term warning. Here we analyse two months of hydraulic-support data from Panel II1013 in the Huaibei mining area and develop a local prediction workflow for support-pressure states. Missing and zero values were repaired, random measurement noise was reduced using a one-dimensional Kalman filter, and normalized sliding-window samples were used to compare CNN, LSTM, CNN-LSTM, Transformer and CNN-LSTM-Attention models against persistence and BP neural network baselines. Data analysis revealed a persistent high-pressure concentration from the middle to upper face. Under matched data partitioning, CNN-LSTM-Attention achieved the lowest test RMSE and highest R

Indexed as

CNN-LSTM-attentionGraded warning assistanceHydraulic-support pressureSteeply inclined longwall miningStrata-pressure prediction

Identifiers

PMID42337304

DOI10.1038/s41598-026-58950-9

PMCPMC13486502

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.