Subtopic Deep Dive
Coal Mine Water Inrush Prediction
Research Guide
What is Coal Mine Water Inrush Prediction?
Coal Mine Water Inrush Prediction develops models to forecast water intrusion risks in coal mines using geological, hydrological, and monitoring data.
Researchers apply fuzzy comprehensive evaluation, attribute synthetic systems, and GIS-based fuzzy set theory for risk assessment. Key methods include secondary fuzzy evaluation (Wang et al., 2012, 203 citations) and attribute synthetic evaluation (Li et al., 2014, 80 citations). Over 10 papers from 2010-2023 focus on floor water inrush and source identification.
Why It Matters
Water inrush causes major mining disasters, with predictive models enabling early warnings to protect workers. Wang et al. (2012) secondary fuzzy evaluation identifies high-risk zones, reducing accidents in coal floors. Li et al. (2014) attribute system and Yang et al. (2017) GIS-fuzzy methods support site-specific safety engineering in China’s coal mines, where inrush events lead to fatalities and production halts.
Key Research Challenges
Heterogeneous Data Integration
Geological, hydrological, and monitoring data vary in scale and format, complicating model inputs. Li et al. (2014) highlight attribute synthesis needs for multi-source fusion. Accurate integration remains critical for reliable predictions.
Uncertainty in Hydrogeology
Fuzzy logic addresses vagueness in aquifer connectivity, but quantification is challenging. Wang et al. (2012) use secondary fuzzy evaluation to handle this, yet real-time application lags. Yang et al. (2016) note persistent issues in water-sand inrush estimation.
Real-Time Model Validation
Models like AHP-CRITIC (Xiao et al., 2022) require field verification, but dynamic mine conditions hinder it. Zhao (2011) fuzzy-SVM faces overfitting in sparse data scenarios. Scalable validation methods are needed for operational use.
Essential Papers
Risk assessment of floor water inrush in coal mines based on secondary fuzzy comprehensive evaluation
Yi Wang, Weifeng Yang, Ming Li et al. · 2012 · International Journal of Rock Mechanics and Mining Sciences · 203 citations
An Attribute Synthetic Evaluation System for Risk Assessment of Floor Water Inrush in Coal Mines
Liping Li, Zongqing Zhou, Shucai Li et al. · 2014 · Mine Water and the Environment · 80 citations
Research on the Mechanism and Control Technology of Coal Wall Sloughing in the Ultra-Large Mining Height Working Face
Xuelong Li, Xinyuan Zhang, Wenlong Shen et al. · 2023 · International Journal of Environmental Research and Public Health · 59 citations
One of the primary factors affecting safe and effective mining in fully mechanized mining faces with large mining heights is coal wall sloughing. This paper establishes the mechanical model of the ...
Risk Assessment of Water Inrush in an Underground Coal Mine Based on GIS and Fuzzy Set Theory
Binbin Yang, Wanghua Sui, Lihong Duan · 2017 · Mine Water and the Environment · 58 citations
Water Resources Utilization and Protection in the Coal Mining Area of Northern China
Shuning Dong, Bin Xu, Shangxian Yin et al. · 2019 · Scientific Reports · 50 citations
Research on Piper-PCA-Bayes-LOOCV discrimination model of water inrush source in mines
Pinghua Huang, Zhongyuan Yang, Xinyi Wang et al. · 2019 · Arabian Journal of Geosciences · 42 citations
Water-conserving mining influencing factors identification and weight determination in northwest China
Fan Li-min, Liqiang Ma, Yihe Yu et al. · 2019 · International Journal of Coal Science & Technology · 41 citations
Abstract Water-conserving mining is an effective way to alleviate the contradiction between fragile ecological environment and high-intensity coal mining in the arid and semi-arid region of northwe...
Reading Guide
Foundational Papers
Start with Wang et al. (2012) for secondary fuzzy evaluation baseline (203 citations), then Li et al. (2014) attribute system (80 citations) for multi-factor assessment, as they establish core risk frameworks.
Recent Advances
Study Xiao et al. (2022) AHP-CRITIC for weighted hazard evaluation and Huang et al. (2019) Piper-PCA-Bayes for source discrimination to capture methodological advances.
Core Methods
Core techniques include fuzzy comprehensive evaluation, attribute synthetic weighting, GIS-fuzzy integration, PCA-Bayes discrimination, and AHP-CRITIC composite methods.
How PapersFlow Helps You Research Coal Mine Water Inrush Prediction
Discover & Search
Research Agent uses searchPapers and citationGraph to map fuzzy evaluation papers from Wang et al. (2012, 203 citations), revealing clusters around Li et al. (2014). exaSearch finds recent extensions like Xiao et al. (2022), while findSimilarPapers expands to GIS-fuzzy works by Yang et al. (2017).
Analyze & Verify
Analysis Agent applies readPaperContent to extract indices from Wang et al. (2012), then verifyResponse with CoVe checks fuzzy membership consistency across Li et al. (2014). runPythonAnalysis recreates Piper-PCA-Bayes models from Huang et al. (2019) using pandas for hydrochemical discrimination, with GRADE scoring evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in real-time prediction beyond static fuzzy models, flagging contradictions in water source ID between Ma et al. (2011) and Huang et al. (2019). Writing Agent uses latexEditText, latexSyncCitations for risk assessment reports, and latexCompile for publication-ready manuscripts with exportMermaid diagrams of inrush mechanisms.
Use Cases
"Reproduce Piper-PCA-Bayes water source discrimination from Huang et al. 2019 with my mine water chemistry data."
Research Agent → searchPapers('Huang 2019') → Analysis Agent → readPaperContent → runPythonAnalysis (pandas PCA on user CSV) → GRADE verification → output: validated discrimination model plot.
"Draft LaTeX report comparing fuzzy evaluation methods for floor water inrush risk."
Research Agent → citationGraph('Wang 2012') → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations(Wang 2012, Li 2014) → latexCompile → output: compiled PDF with cited comparisons.
"Find GitHub repos implementing AHP-CRITIC water inrush models from recent papers."
Research Agent → searchPapers('Xiao 2022 AHP-CRITIC') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → output: repo links with code for weight determination.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'coal mine water inrush fuzzy', producing structured reports ranking Wang et al. (2012) by citations. DeepScan applies 7-step CoVe to verify Huang et al. (2019) Piper-PCA against field data with runPythonAnalysis checkpoints. Theorizer generates hypotheses linking Xiao et al. (2022) AHP-CRITIC to real-time monitoring gaps.
Frequently Asked Questions
What is coal mine water inrush prediction?
It forecasts water intrusion risks using geological and hydrological models like fuzzy comprehensive evaluation.
What are main methods used?
Secondary fuzzy evaluation (Wang et al., 2012), attribute synthetic systems (Li et al., 2014), and Piper-PCA-Bayes (Huang et al., 2019) discriminate sources and assess risks.
What are key papers?
Wang et al. (2012, 203 citations) on fuzzy evaluation; Li et al. (2014, 80 citations) on attribute systems; Xiao et al. (2022, 38 citations) on AHP-CRITIC weighting.
What open problems exist?
Real-time validation of models under dynamic conditions and integration of multi-source data for operational early warnings persist as challenges.
Research Geoscience and Mining Technology with AI
PapersFlow provides specialized AI tools for Engineering researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Paper Summarizer
Get structured summaries of any paper in seconds
Code & Data Discovery
Find datasets, code repositories, and computational tools
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Engineering use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Coal Mine Water Inrush Prediction with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Engineering researchers
Part of the Geoscience and Mining Technology Research Guide