Evidence map›Paper›PMID 41305064›Full record

ArticleSensors (Basel, Switzerland)2025

Application of Multi-Sensor Data Fusion and Machine Learning for Early Warning of Cambrian Limestone Water Hazards.

Hang Li, Yijia Li, Wantong Lin, Huaixiang Yang, Kefeng Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Hang LiInner Mongolia Research Institute, China University of Mining and Technology (Beijing), Beijing 100083, China.
Yijia LiInner Mongolia Research Institute, China University of Mining and Technology (Beijing), Beijing 100083, China.
Wantong LinInner Mongolia Research Institute, China University of Mining and Technology (Beijing), Beijing 100083, China.
Huaixiang YangInner Mongolia Research Institute, China University of Mining and Technology (Beijing), Beijing 100083, China.
Kefeng LiuInner Mongolia Research Institute, China University of Mining and Technology (Beijing), Beijing 100083, China.

Funding

the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB) No. BBJ2025008
6 · The paper itself

Abstract

The issue of water disasters in the mining floor is extremely severe. Despite significant progress in the on-site monitoring and identification of water inrush channels, research on the spatial development characteristics of cracks and the temporal evolution patterns remains insufficient, resulting in the incomplete development of microseismic-based water disaster early warning theory and practice. Based on this, the present study first derives the expressions for the diameter and length of water inrush channels according to the damage characteristics of microseismic events and the glazed porcelain shape features of the channels. A theoretical model for the correlation between microseismic-water inrush volume is established, and the relationship between microseismic and water level is revealed. Analysis of field monitoring data further indicates that when high-energy microseismic features (such as single high-energy events and higher daily cumulative energy) are detected, the aquifer water level begins to decline, followed by high water inrush events. Therefore, a decrease in water level accompanied by high-energy microseismic features can serve as an important early warning marker for water disasters. Finally, advanced machine learning methods are applied, in which the optimal index combination for floor water inrush prediction is obtained through the genetic algorithm, and the weights of each index are determined by integrating the analytic hierarchy process with the random forest model. Field engineering verification demonstrates that the integrated early warning system performs significantly better than any single monitoring indicator, and all high-water-inrush events are successfully predicted within four days.

Indexed as

Cambrian limestone aquiferfloor water inrush early warninggenetic algorithm optimizationindicator weight distributionmicroseismic-water inrush volume model

Identifiers

PMID41305064
PMCPMC12655997

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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.