Subtopic Deep Dive
Risk Assessment of Water Inrush in Coal Mines
Research Guide
What is Risk Assessment of Water Inrush in Coal Mines?
Risk Assessment of Water Inrush in Coal Mines evaluates the probability of sudden floor water intrusion during underground coal extraction using fuzzy comprehensive evaluation, geological modeling, and probabilistic methods.
This subtopic integrates hydrogeological data, fault analysis, and models like MFIM-TOPSIS and set pair analysis for early warning systems. Key papers include Guanda Zhang et al. (2021) with 36 citations on MFIM-TOPSIS variable weight model and Bo Li et al. (2017) with 32 citations on set pair analysis. Recent works like Chengyue Gao et al. (2024) apply moving window methods for local assessments.
Why It Matters
Water inrush accidents cause fatalities and economic losses in coal mining, with prevention models reducing risks during blasting and extraction. Guanda Zhang et al. (2021) MFIM-TOPSIS model identifies high-risk zones in seam floors, enabling targeted grouting. Bo Li et al. (2017) set pair analysis integrates multisource data for probabilistic forecasting, applied in Chinese mines to avoid disasters. Dangliang Wang et al. (2023) GIS-MCDA method accounts for decision-maker attitudes, improving spatial risk maps for operational safety.
Key Research Challenges
Variable Weight Determination
Constant weight models ignore spatial differences in indicator importance, leading to inaccurate global assessments. Chengyue Gao et al. (2024) highlight moving window methods to address local variations. Bo Li et al. (2021) propose variable weight models (VWM) for multisource fusion.
Risk-Coping Attitude Integration
Standard MCDA overlooks decision-makers' risk attitudes, causing biased evaluations. Dangliang Wang et al. (2023) develop probabilistic GIS methods incorporating attitudes. This improves roof water inrush predictions under uncertainty.
Dynamic Pore Pressure Modeling
Periodic blasting loads distort aquiclude pore pressures, complicating real-time warnings. Tao Yang et al. (2021) simulate spatial-temporal characteristics for shallow seams. Models must predict inrush from dynamic hydrogeological changes.
Essential Papers
Risk assessment of floor water inrush in coal mines based on MFIM-TOPSIS variable weight model
Guanda Zhang, Yiguo Xue, Chenghao Bai et al. · 2021 · Journal of Central South University · 36 citations
Risk Evaluation Model of Highway Tunnel Portal Construction Based on BP Fuzzy Neural Network
Xianghui Deng, Tian Xu, Rui Wang · 2018 · Computational Intelligence and Neuroscience · 33 citations
Risk assessment for tunnel portals in the construction stage has been widely recognized as one of the most critical phases in tunnel construction as it easily causes accident than the overall lengt...
Risk Analysis Model of Water Inrush Through the Seam Floor Based on Set Pair Analysis
Bo Li, Qiang Wu, Xianqian Duan et al. · 2017 · Mine Water and the Environment · 32 citations
A GIS-Based Probabilistic Spatial Multicriteria Roof Water Inrush Risk Evaluation Method Considering Decision Makers’ Risk-Coping Attitude
Dangliang Wang, Chengyue Gao, Kerui Liu et al. · 2023 · Water · 10 citations
A combination of geographic information system (GIS) and spatial multicriteria decision making (MCDA) in mine water inrush risk evaluation is widely used, but the randomness in the process of index...
A Simulation Study on the Spatial‐Temporal Characteristics of Pore Water Pressure and Roof Water Inrush in an Aquiclude
Tao Yang, Ji Li, Longwen Wan et al. · 2021 · Shock and Vibration · 6 citations
As the working face advances, the overlying aquiclude is subjected to periodic dynamic loads, causing pore water pressure distortion, which provides important forewarning for a water inrush disaste...
Local Water Inrush Risk Assessment Method Based on Moving Window and Its Application in the Liangshuijing Mining Area
Chengyue Gao, Dangliang Wang, Jin Ma et al. · 2024 · Water · 6 citations
Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indi...
Multisource Information Risk Evaluation Technology of Mine Water Inrush Based on VWM: A Case Study of Weng’an Coal Mine
Bo Li, Tao Li, Wenping Zhang et al. · 2021 · Geofluids · 5 citations
The use of multisource information fusion technology to predict the risk of water inrush from coal floor is a research hotspot in recent years, but the current evaluation method is mainly based on ...
Reading Guide
Foundational Papers
No pre-2015 foundational papers available; start with highest-cited recent: Guanda Zhang et al. (2021) for MFIM-TOPSIS basics and Bo Li et al. (2017) for set pair analysis fundamentals.
Recent Advances
Chengyue Gao et al. (2024) for moving window local risks; Dangliang Wang et al. (2023) for GIS probabilistic attitudes; Ming Sun (2024) for seam-specific factors.
Core Methods
Fuzzy comprehensive evaluation (Deng et al., 2018), variable weight models (Li et al., 2021), set pair analysis (Li et al., 2017), GIS-MCDA (Wang et al., 2023), and pore pressure simulation (Yang et al., 2021).
How PapersFlow Helps You Research Risk Assessment of Water Inrush in Coal Mines
Discover & Search
Research Agent uses searchPapers with query 'floor water inrush MFIM-TOPSIS' to retrieve Guanda Zhang et al. (2021) (36 citations), then citationGraph reveals Bo Li et al. (2017) influencers, and findSimilarPapers uncovers Chengyue Gao et al. (2024) local assessment extensions.
Analyze & Verify
Analysis Agent applies readPaperContent on Guanda Zhang et al. (2021) to extract MFIM-TOPSIS weights, verifies response with CoVe against Bo Li et al. (2017) set pair formulas, and runPythonAnalysis recreates variable weight computations using NumPy/pandas for statistical validation with GRADE scoring on model accuracy.
Synthesize & Write
Synthesis Agent detects gaps in constant vs. variable weight models across Zhang (2021) and Li (2021), flags contradictions in risk attitudes from Wang (2023); Writing Agent uses latexEditText for evaluation matrices, latexSyncCitations for 10+ papers, and latexCompile to generate risk assessment reports with exportMermaid flowcharts of inrush pathways.
Use Cases
"Reproduce MFIM-TOPSIS variable weight model from Zhang 2021 with Python"
Research Agent → searchPapers → readPaperContent → Analysis Agent → runPythonAnalysis (NumPy/pandas matrix ops, matplotlib risk heatmaps) → researcher gets executable code and validation plots.
"Write LaTeX report on water inrush risks citing Gao 2024 and Wang 2023"
Synthesis Agent → gap detection → Writing Agent → latexEditText (add methods section) → latexSyncCitations (10 papers) → latexCompile → researcher gets PDF with cited risk maps and diagrams.
"Find GitHub repos implementing set pair analysis for mine water inrush"
Research Agent → searchPapers (Bo Li 2017) → paperExtractUrls → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets repo code, inspection report, and runPythonAnalysis test results.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'coal mine floor water inrush', structures report with MFIM-TOPSIS synthesis from Zhang (2021). DeepScan applies 7-step CoVe to verify Gao (2024) moving window against Li (2021) VWM, with GRADE checkpoints. Theorizer generates new variable weight theories from Wang (2023) attitudes and Yang (2021) simulations.
Frequently Asked Questions
What is the definition of risk assessment of water inrush in coal mines?
It evaluates floor water intrusion risks during coal extraction using fuzzy evaluation, geological modeling, and probabilistic methods like MFIM-TOPSIS (Zhang et al., 2021).
What are key methods used?
MFIM-TOPSIS variable weight (Zhang et al., 2021), set pair analysis (Li et al., 2017), GIS-MCDA with attitudes (Wang et al., 2023), and moving window local assessment (Gao et al., 2024).
What are the most cited papers?
Guanda Zhang et al. (2021, 36 citations) on MFIM-TOPSIS; Bo Li et al. (2017, 32 citations) on set pair analysis; Xianghui Deng et al. (2018, 33 citations) on BP fuzzy neural networks.
What are open problems?
Integrating dynamic blasting loads into models (Yang et al., 2021), handling risk attitudes in MCDA (Wang et al., 2023), and scaling local moving window methods globally (Gao et al., 2024).
Research Blasting Impact and Analysis 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 Risk Assessment of Water Inrush in Coal Mines 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 Blasting Impact and Analysis Research Guide