IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v290y2024ics0360544223035946.html
   My bibliography  Save this article

Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection

Author

Listed:
  • Dong, Fangying
  • Yin, Huiyong
  • Cheng, Wenju
  • Zhang, Chao
  • Zhang, Danyang
  • Ding, Haixiao
  • Lu, Chang
  • Wang, Yin

Abstract

As one of the most abundant fossil fuels in the world, the safe and efficient mining of coal resources has greatly affects all fields of human production and life. Accurate prediction of the water yield capacity (WYC) of coal seam roofs and advanced detection of water abundance is of great significance to preventing roof water disasters in coal mines. In the process of coal mining, complex roof water inrush mechanism, and changeable factors are faced. Moreover, the uncertainty of the interconnection between various factors increases the difficulty of roof water disaster prevention. To improve the accuracy and reliability of WYC prediction, this study analyzed the main control factors of coal seam roof water inrush and built a WYC quantitative prediction model based on the SAA-CNN algorithm. 47 samples (40 training sets and 7 test sets) were selected to train and learn the model, and the WYC of the 3rd coal mining roof in Jiangzhuang Coal Mine (JZCM) was predicted. To verify the reliability of the model, BP, RF, SVM, CNN, and SAA-CNN models were used for training and error comparison. Finally, the 1001 working face of JZCM was selected to use a variety of geophysical methods and drilling combined for advanced detection and construct the dynamic monitoring and prewarning system of coal seam roof water inrush risk. The results show that the accuracy of the WYC prediction model is SAA-CNN＞RF＞CNN＞SVM＞BP. The average absolute error of the SAA-CNN model is 0.87 m3/h, and the average relative error is 3.56 %. In this study, the quantitative and accurate prediction of WYC of coal seam roofs is realized. Combined with the advanced detection method of “electric-magnetic-drilling”, the prewarning system of water inrush risk of mining is constructed, which provides a new method for the prevention of water disasters in coal seam roofs.

Suggested Citation

  • Dong, Fangying & Yin, Huiyong & Cheng, Wenju & Zhang, Chao & Zhang, Danyang & Ding, Haixiao & Lu, Chang & Wang, Yin, 2024. "Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035946
    DOI: 10.1016/j.energy.2023.130200
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223035946
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.130200?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guohua Zhang & Wenyan Xing & Yanwei Duan & Tao Qin & Xiangang Hou, 2023. "Prediction and Application of the Height of Water-Conducting Fracture Zone in the Composite Roof: A Case Study of Jinxinda Coal Mine," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    2. Song, Yunting & Wang, Nuo, 2019. "Exploring temporal and spatial evolution of global coal supply-demand and flow structure," Energy, Elsevier, vol. 168(C), pages 1073-1080.
    3. Huiyong Yin & Fangying Dong & Yiwen Zhang & Wenju Cheng & Peihe Zhai & Xuyan Ren & Ziang Liu & Yutao Zhai & Xin Li, 2022. "Height Prediction and 3D Visualization of Mining-Induced Water-Conducting Fracture Zone in Western Ordos Basin Based on a Multi-Factor Regression Analysis," Energies, MDPI, vol. 15(11), pages 1-16, May.
    4. Lele Xiao & Fan Li & Chao Niu & Gelian Dai & Qian Qiao & Chengsen Lin, 2022. "Evaluation of Water Inrush Hazard in Coal Seam Roof Based on the AHP-CRITIC Composite Weighted Method," Energies, MDPI, vol. 16(1), pages 1-20, December.
    5. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    6. Liu, Tianhao & Xu, Dongjing & Shi, Longqing & Qu, Linyan & Ji, Kaiming, 2022. "Trapezoidal collapse model to calculate the height of the overburden collapse zone in coal seam mining: An example from Guo'Jiahe Coal Mine, Western China," Energy, Elsevier, vol. 256(C).
    7. He, Jianming & Li, Xiao & Yin, Chao & Zhang, Yixiang & Lin, Chong, 2020. "Propagation and characterization of the micro cracks induced by hydraulic fracturing in shale," Energy, Elsevier, vol. 191(C).
    8. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Wang, Chongyang & Zhang, Dongming & Liu, Chenxi & Pan, Yisha & Jiang, Zhigang & Yu, Beichen & Lin, Yun, 2023. "Deformation and seepage characteristics of water-saturated shale under true triaxial stress," Energy, Elsevier, vol. 284(C).
    3. Liang, Cun-Guang & Guo, Ze-Shi & Yue, Xiu & Li, Hui & Ma, Peng-Cheng, 2023. "Microwave-assisted breakage of basalt: A viewpoint on analyzing the thermal and mechanical behavior of rock," Energy, Elsevier, vol. 273(C).
    4. Cui, Song & Liu, Songyong & Li, Hongsheng & Zhou, Fangyue & Sun, Dunkai, 2022. "Critical parameters investigation of rock breaking by high-pressure foam fracturing method," Energy, Elsevier, vol. 258(C).
    5. Tao Yan & Chuanqu Zhu & Qingfeng Li & Qian Xu, 2023. "Investigating Disaster Mechanisms Triggered by Abrupt Overburden Fracture Alterations in Close-Seam Mining Beneath an Exceptionally Thick Sandstone Aquifer," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    6. Jiang, Xingwen & Chen, Mian & Li, Qinghui & Liang, Lihao & Zhong, Zhen & Yu, Bo & Wen, Hang, 2022. "Study on the feasibility of the heat treatment after shale gas reservoir hydration fracturing," Energy, Elsevier, vol. 254(PB).
    7. Gou, Qiyang & Xu, Shang & Hao, Fang & Yang, Feng & Shu, Zhiguo & Liu, Rui, 2021. "The effect of tectonic deformation and preservation condition on the shale pore structure using adsorption-based textural quantification and 3D image observation," Energy, Elsevier, vol. 219(C).
    8. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    9. Aniket Vatsa & Ananda Shankar Hati & Vadim Bolshev & Alexander Vinogradov & Vladimir Panchenko & Prasun Chakrabarti, 2023. "Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks," Energies, MDPI, vol. 16(5), pages 1-14, March.
    10. Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
    11. Lan, Wenjian & Wang, Hanxiang & Zhang, Xin & Fan, Hongbo & Feng, Kun & Liu, Yanxin & Sun, Bingyu, 2020. "Investigation on the mechanism of micro-cracks generated by microwave heating in coal and rock," Energy, Elsevier, vol. 206(C).
    12. Cheng, Ming & Fu, Xuehai & Chen, Zhaoying & Liu, Ting & Zhang, Miao & Kang, Junqiang, 2023. "A new approach to evaluate abandoned mine methane resources based on the zoning of the mining-disturbed strata," Energy, Elsevier, vol. 274(C).
    13. Liyang Bai & Changlong Liao & Changxiang Wang & Meng Zhang & Fanbao Meng & Mingjin Fan & Baoliang Zhang, 2022. "Study on Height Prediction of Water Flowing Fractured Zone in Deep Mines Based on Weka Platform," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    14. Zhou, Aitao & Li, Jingwen & Gong, Weili & Wang, Kai & Du, Changang, 2023. "Theoretical and numerical study on the contribution of multi-hole arrangement to coalbed methane extraction," Energy, Elsevier, vol. 284(C).
    15. Zheng, Peng & Xia, Yucheng & Yao, Tingwei & Jiang, Xu & Xiao, Peiyao & He, Zexuan & Zhou, Desheng, 2022. "Formation mechanisms of hydraulic fracture network based on fracture interaction," Energy, Elsevier, vol. 243(C).
    16. Zhao, Ruijia & Xie, Xinlian & Li, Xinyang & Li, Guodong, 2020. "Game-theoretical models of competition analysis and pricing strategy for two modes for repairing damaged marine structures at sea," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    17. Wang, Wenya & Fan, L.W. & Zhou, P., 2022. "Evolution of global fossil fuel trade dependencies," Energy, Elsevier, vol. 238(PC).
    18. Pahari, Silabrata & Bhandakkar, Parth & Akbulut, Mustafa & Sang-Il Kwon, Joseph, 2021. "Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs," Energy, Elsevier, vol. 216(C).
    19. Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).
    20. Wang, Wenya & Fan, Liwei & Li, Zhenfu & Zhou, Peng & Chen, Xue, 2021. "Measuring dynamic competitive relationship and intensity among the global coal importing trade," Applied Energy, Elsevier, vol. 303(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035946. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.