Abstract
In view of the difficulty and low efficiency of models established with big data, a deep feedforward network-based mine water inrush discriminant model is established. The stochastic gradient descent (SGD) algorithm is employed to optimize model parameter training. The cross-entropy loss and accuracy of the training and test samples are considered to characterize the model training effect. To prevent overfitting during model training, the dropout optimization method is incorporated into the hidden layers of the model. The four main water-filled aquifers in the Huainan Panxie mining area are chosen as discrimination objects, 1952 sets of water samples are applied as modeling data, and a total of nine indicators, including K+ + Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3−, CO32−, pH, and total dissolved solids (TDS), are selected as discriminating factors. Through model training, the dropout rate, epoch number, batch size, and learning rate are set as 0.1, 90, 25, and 10−2, respectively, and the discriminant accuracy rates for the training and test samples are 93.34% and 96.68%, respectively. The trained model is applied to discriminate 30 groups of water samples, and the discrimination results for 28 groups of water samples are consistent with observations. The results indicate that the deep learning discriminant model based on big data achieves high accuracy, good applicability, and a suitable discrimination ability, and provides a certain guiding significance in the prevention and control of water inrush hazards and related field work.
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References
Adem K, Kiliçarslan S, Comert O (2019) Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Syst Appl 115:557–564. https://doi.org/10.1016/j.eswa.2018.08.050
Bai HB, Miao XX (2016) Hydrogological characteristics and mine water inrush prevention of late Paleozoic coalfields. J China U Min Techno 45(1):1–10. https://doi.org/10.13247/j.cnki.jcumt.000450 (in Chinese)
Barbastathis G, Ozcan A, Situ G (2019) On the use of deep learning for computational imaging. Optica 6(8):921–943. https://doi.org/10.1364/OPTICA.6.000921
Bi YS, Wu JW, Zhai XR, Wang GT, Shen SH, Qing XB (2021) Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis. Environ Earth Sci 80(4):144. https://doi.org/10.1007/s12665-021-09450-8
Chen X, Jiang CL, Zheng LG, Zhang LQ, Fu XJ, Cheng SG, Chen YC, Hu J (2021) Evaluating the genesis and dominant processes of groundwater salinization by using hydrochemistry and multiple isotopes in a mining city. Environ Pollut 283:117381. https://doi.org/10.1016/j.envpol.2021.117381
Dong DL, Chen ZY, Lin G, Lin X, Zhang RM, Ji Y (2019) Combining the fisher feature extraction and support vector machine methods to identify the water inrush source: a case study of the Wuhai mining area. Mine Water Environ 38(4):855–862. https://doi.org/10.1007/s10230-019-00637-x
Dong SN, Wang H, Guo XM, Zhou ZF (2021) Characteristics of water hazards in China’s coal mines: a review. Mine Water Environ 40(2):325–333. https://doi.org/10.1007/S10230-021-00770-6
Eckle K, Schmidt-Hieber J (2019) A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Netw 110:232–242. https://doi.org/10.1016/j.neunet.2018.11.005
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29. https://doi.org/10.1038/s41591-018-0316-z
Guan ZL, Jia ZF, Zhao ZQ, You QY (2019) Identification of inrush water recharge sources using hydrochemistry and stable isotopes: a case study of Mindong No. 1 coal mine in north-east Inner Mongolia, China. J Earth Syst Sci 128(7):1–12. https://doi.org/10.1007/s12040-019-1232-4
Hoeser T, Bachofer F, Kuenzer C (2020) Object detection and image segmentation with deep learning on earth observation data: a review-part II: applications. Remote Sens 12(18):3053. https://doi.org/10.3390/rs12183053
Hou EK, Wen Q, Chen CXY, W, Wei JB, Ye ZN (2020) Study on recognition of mine water sources based on statistical analysis. Arab J Geosci 13(1):1–12. https://doi.org/10.1007/s12517-019-4984-x
Hu K, Zhang ZZ, Niu XR, Zhang Y, Cao CH, Xiao F, Gao XP (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179–191. https://doi.org/10.1016/j.neucom.2018.05.011
Huang PH, Wang XY (2018) Piper-PCA-Fisher recognition model of water inrush source: a case study of the Jiaozuo mining area. Geofluids 198:9205025. https://doi.org/10.1155/2018/9205025
Huang PH, Wang XY, Han SM (2017) Recognition model of groundwater inrush source of coal mine: a case study on Jiaozuo coal mine in China. Arab J Geosci 10(15):323. https://doi.org/10.1007/s12517-017-3099-5
Huang PH, Yang ZY, Wang XY, Ding FF (2019) Research on Piper-PCA-Bayes-LOOCV discrimination model of water inrush source in mines. Arab J Geosci 12(11):1–11. https://doi.org/10.1007/s12517-019-4500-3
Jiang CL, An YQ, Zheng LG, Huang WW (2021) Water source discrimination in a multiaquifer mine using a comprehensive stepwise discriminant method. Mine Water Environ 40:442–455. https://doi.org/10.1007/S10230-020-00742-2
Jiang AN, Liang B (2006) The particle swarm optimization support vectors machine method of identifying standard components of ions of groundwater. J China Coal Soc 31(3):310–313. https://doi.org/10.3321/j.issn:0253-9993.2006.03.009(inChinese)
Li B, Wu Q (2020) Liu ZJ (2020) Identification of mine water inrush source based on PCA-FDA: Xiandewang coal mine case. Geofluids 2:1–8. https://doi.org/10.1155/2020/2584094
Li YZ, Niu GQ, Liu HL (2016) Application of improved GA-BP neural network on identification of water inrush source in mine. J Safety Sci Tech 12(7):77–81. https://doi.org/10.11731/j.issn.1673-193x.2016.07.014 (in Chinese)
Liu GW, Ma FS, Liu G, Zhao HJ, Guo J, Cao JY (2019) Application of multivariate statistical analysis to identify water sources in a coastal gold mine, Shandong, China. Sustainability 11(12):1–17. https://doi.org/10.3390/su11123345
Liu P, Nils H, Carsten D, Sun YJ, Xu ZM (2017) Hydro-geochemical paths of multi-layer groundwater system in coal mining regions-using multivariate statistics and geochemical modeling approaches. Sci Total Environ 601:1–14. https://doi.org/10.1016/j.scitotenv.2017.05.146
Lu JT, Li XB, Gong FQ, Wang XR, Liu J (2012) Recognizing of mine water inrush sources based on principal components analysis and fisher discrimination analysis method. China Saf Sci J 22(7):109–115. https://doi.org/10.16265/j.cnki.issn1003-3033.2012.07.016 (in Chinese)
Meng G, Cong W, Zhu C (2016) Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Pr 72–73:92–104. https://doi.org/10.1016/j.ymssp.2015.11.014
Piotrowski AP, Napiorkowski JJ, Piotrowska AE (2019) Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling. Earth-Sci Rev 201:103076. https://doi.org/10.1016/j.earscirev.2019.103076
Qian JZ, Tong Y, Ma L, ZhaoWD ZRG, He XR (2018) Hydrochemical characteristics and groundwater source identification of a multiple aquifer system in a coal mine. Mine Water Environ 37(3):528–540. https://doi.org/10.1007/s10230-017-0493-x
Qiu M, Shi LQ, Teng C, Zhou Y (2017) Assessment of water inrush risk using the fuzzy Delphi analytic hierarchy process and grey relational analysis in the Liangzhuang Coal Mine, China. Mine Water Environ 36(1):39–50. https://doi.org/10.1007/s10230-016-0391-7
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat, (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204. https://doi.org/10.1038/s41586-019-0912-1
Wang D, Shi L (2019) Source identification of mine water inrush: a discussion on the application of hydrochemical method. Arab J Geosci 12(2):1–12. https://doi.org/10.1007/s12517-018-4076-3
Wang XY, Xu T, Huang D (2011) Application of distance discriminance in identifying water inrush resource in similar coalmine. J China Coal Soc 36(8):1354–1358. https://doi.org/10.13225/j.cnki.jccs.2011.08.028 (in Chinese)
Wang Y, Shi LQ, Wang M, Liu TH (2020) Hydrochemical analysis and discrimination of mine water source of the Jiaojia gold mine area, China. Environ Earth Sci 79(6):1–14. https://doi.org/10.1007/s12665-020-8856-1
Wang Y, Yang WF, Li M, Liu X (2012) Risk assessment of floor water inrush in coal mines based on secondary fuzzy comprehensive evaluation. Int J Rock Mech Min 52:50–55. https://doi.org/10.1016/j.ijrmms.2012.03.006
Wu Q, Liu YZ, Liu DH, Zhou WF (2011) Prediction of floor water inrush: the application of GIS-based AHP vulnerable index method to Donghuantuo coal mine. China Rock Mech Rock Eng 44(5):591–600. https://doi.org/10.1007/s00603-011-0146-5
Wu Q, Mu WP, Xing Y, Qian C, Shen JJ, Wang Y, Zhao KD (2019a) Source discrimination of mine water inrush using multiple methods: a case study from the Beiyangzhuang Mine. Northern China. B Eng Geol Environ 78(1):469–482. https://doi.org/10.1007/s10064-017-1194-1
Wu Q, Tu K, Zeng YF, Liu SQ (2019) Discussion on the main problems and countermeasures for building an upgrade version of main energy (coal) industry in China. J China Coal Soc 44(6):1625–1636. https://doi.org/10.13225/j.cnki.jccs.2019.0387 (in Chinese)
Xu YC, Luo YQ, Li JH, Li KQ, Cao XC (2018) Water and sand inrush during mining under thick unconsolidated layers and thin bedrock in the Zhaogu No. 1 coal mine, China. Mine water Environ 37(2):336–345. https://doi.org/10.1007/s10230-018-0539-8
Yan BQ, Ren FH, Cai MF, Qiao C (2020a) Bayesian model based on Markov chain Monte Carlo for identifying mine water sources in submarine gold mining. J Cleaner Prod 253:1–10. https://doi.org/10.1016/j.jclepro.2020.120008
Yan XQ, Hu SZ, Mao YQ, Ye YD, Yu H (2021) Deep multi-view learning methods: a review. Neurocomputing 448:106–129. https://doi.org/10.1016/j.neucom.2021.03.090
Yan Z, Chen JD, Hu R, Huang TW, Chen YR, Wen SP (2020b) Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates. Neural Netw 128:142–149. https://doi.org/10.1016/j.neunet.2020.04.025
Yan ZG, Bai HB (2009) MMH support vector machines model for recognizing multi-headstream of water inrush in mine. Chin J Rock Mech Eng 28(2):324–329. https://doi.org/10.3321/j.issn:1000-6915.2009.02.015 (inChinese)
Yang Y, Yue JH, Li J, Yang Z (2018) Mine water inrush sources online discrimination model using fluorescence spectrum and CNN. IEEE Access 6:47828–47835. https://doi.org/10.1109/ACCESS.2018.2866506
Yu SC, Xu JM, Zhu WB, Wang SH, Liu WB (2020) Development of a combined mining technique to protect the underground workspace above confined aquifer from water inrush disaster. B Eng Geol Environ 79(7):3649–3666. https://doi.org/10.1007/s10064-020-01803-0
Zhang H, Xing HF, Yao DX, Liu LL, Xue DR, Guo F (2019) The multiple logistic regression recognition model for mine water inrush source based on cluster analysis. Environ Earth Sci 78(20):612. https://doi.org/10.1007/s12665-019-8624-2
Zhang H, Yao DX (2020) The Bayes recognition model for mine water inrush source based on multiple logistic regression analysis. Mine Water Environ 39(4):888–901. https://doi.org/10.1007/s10230-020-00699-2
Zhang HT, Xu GQ, Chen XQ, Mabaire A, Zhou JS, Zhang G, Zhu L (2020a) Groundwater hydrogeochemical processes and the connectivity of multilayer aquifers in a coal mine with karst collapse columns. Mine Water Environ 39:356–368. https://doi.org/10.1007/s10230-020-00667-w
Zhang J, Chen LW, Chen YF, Ge RT, Ma L, Zhou KD, Shi XP (2020b) Discrimination of water-inrush source and evolution analysis of hydrochemical environment under mining in Renlou coal mine, Anhui Province, China. Environ Earth Sci 79(2):1–13. https://doi.org/10.1007/s12665-019-8803-1
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Our deepest gratitude goes to the editors and anonymous reviewers for their careful work and thoughtful suggestions that helped improve this paper substantially.
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The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (41602310) and China Postdoctoral Science Foundation (2017M611044).
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Jiang, C., Zhu, S., Hu, H. et al. Deep learning model based on big data for water source discrimination in an underground multiaquifer coal mine. Bull Eng Geol Environ 81, 26 (2022). https://doi.org/10.1007/s10064-021-02535-5
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DOI: https://doi.org/10.1007/s10064-021-02535-5