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College of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing, China
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Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition

Abstract submission deadline
28 December 2025
Manuscript submission deadline
28 February 2026
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379

Topic Information

Dear Colleagues,

There are many types of dynamic disasters, including water inrush, coal and gas outburst, rock burst, mine shock, etc. These various types of dynamic disasters occur in a variety of geological conditions. In addition, mining layout, mining methods, mining intensity, pressure relief control technology, etc., have an significant impact on the occurrence and prediction of dynamic disasters. The coupling of human mining activities and nature makes the mechanism of dynamic disasters complex, prediction accuracy low, and control difficult.

Although there are a number research articles on dynamic disasters, there is still considerable room for debate. Some of the frequently debated topics include:

  • The influence of geological structure on the occurrence of dynamic disasters;
  • Dynamic disaster control technology;
  • Dynamic disaster occurrence mechanisms;
  • Deep learning and intelligent identification of dynamic disaster occurrence laws based on big data;
  • Common scientific problems of solid, liquid, and gas three-phase medium power disasters;
  • Intelligent monitoring and early warning of dynamic disaster precursor characteristics.

Contributions covering any of these topics, including the perspective of the coordinated regulation of dynamic disasters and other disasters, are welcome, as are contributions using seismology knowledge to predict dynamic disasters.

We invite you to share your experience, field research results, and indicators obtained through the analysis of dynamic disaster occurrence mechanisms with your colleagues and contribute to strengthening our technological innovation in dynamic disaster occurrence mechanisms, early warning, regulation, and disaster reduction.

Prof. Dr. Feng Cui
Dr. Zhenlei Li
Topic Editors

Keywords

  • mechanism of dynamic disaster
  • dynamic disaster control
  • multi-field coupling
  • mine multi-source disaster monitoring
  • intelligent analysis
  • mineral exploration

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.5 2011 19.8 Days CHF 2400 Submit
Energies
energies 		3.2 7.3 2008 16.2 Days CHF 2600 Submit
GeoHazards
geohazards 		1.6 2.2 2020 17.2 Days CHF 1400 Submit
Minerals
minerals 		2.2 4.4 2011 18.2 Days CHF 2400 Submit

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Published Papers (1 paper)

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Article
Application of the CPO-CNN-BILSTM Hybrid Model for Evaluation of Water Abundance of the Roof Aquifer—A Case Study of WoBei Mine in Huaibei Coalfield, China
by Yuchu Liu, Qiqing Wang, Jingzhong Zhu, Dongding Li and Wenping Li
Appl. Sci. 2025, 15(21), 11816; https://doi.org/10.3390/app152111816 - 5 Nov 2025
Abstract
With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the [...] Read more.
With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the loose strata. The aquifers are mainly composed of unconsolidated sand, gravel, and weakly consolidated marl, which has strong permeability and an extremely unfavorable impact on safe production. Identifying the target area to prevent and control roof water damage can reduce the likelihood of water damage accidents in mines. This study takes the 85 mining district of Wobei mine as an engineering case. The discriminant indexes are selected for aquifer thickness, gradation coefficient, marlstone thickness, permeability, grouting quantity, and grouting termination pressure. A model integrating the newly proposed Crowned Porcupine Optimization (CPO, 2024), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) was constructed to predict unit water influx. A zonal map was generated based on the expected unit water influx of the fourth aquifer after grouting. In addition, the prediction results are compared with those from other models. Results indicate that the CPO-CNN-BiLSTM model achieves a higher accuracy and fewer errors in water abundance prediction, with an RMSE of 2.58 × 10−5 and an R2 of 0.982 for the testing dataset. According to the prediction result, the fourth aquifer after grouting in the 85 mining district is divided into five water abundance zones. The strong and medium–strong water abundance zones are mainly distributed in the study area’s eastern region. A small portion of them is distributed in the northwestern and northern areas. This study provides a new insight for predicting the water abundance of thick loose aquifers and a theoretical basis for safe mining under thick loose aquifers. Full article
(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition)
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