{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:15:50Z","timestamp":1776284150960,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Key R &D Program of China","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}]},{"name":"S &T Program of Hebei","award":["20310101D"],"award-info":[{"award-number":["20310101D"]}]},{"name":"Natural Science Foundation of Beijing Municipality","award":["4222030"],"award-info":[{"award-number":["4222030"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s13042-022-01650-3","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T19:02:35Z","timestamp":1664737355000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Knowledge-based and data-driven underground pressure forecasting based on graph structure learning"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-2852","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haohua","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"1650_CR1","unstructured":"Song W, Cheng J, Wang W, Qin Y, Wang Z, Borowski M, Wang Y, Tukkaraja P (2021)Underground mine gas explosion accidents and prevention techniques\u2014an overview. Arch Min Sci 66(2)"},{"key":"1650_CR2","doi-asserted-by":"crossref","unstructured":"Mohanty D (2017) An overview of the geological controls in underground coal gasification. In: IOP conference series: earth and environmental science, vol 76, p 012010, IOP Publishing","DOI":"10.1088\/1755-1315\/76\/1\/012010"},{"key":"1650_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.isatra.2018.01.007","volume":"74","author":"H Liu","year":"2018","unstructured":"Liu H, Yu H (2018) Decentralized state estimation for a large-scale spatially interconnected system. ISA Trans 74:67\u201376","journal-title":"ISA Trans"},{"issue":"15","key":"1650_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-017-6878-0","volume":"76","author":"X Yu","year":"2017","unstructured":"Yu X, Han J, Shi L, Wang Y, Zhao Y (2017) Application of a bp neural network in predicting destroyed floor depth caused by underground pressure. Environ Earth Sci 76(15):1\u201312","journal-title":"Environ Earth Sci"},{"issue":"9","key":"1650_CR5","doi-asserted-by":"publisher","first-page":"6717","DOI":"10.1007\/s12665-015-4682-2","volume":"74","author":"W Wang","year":"2015","unstructured":"Wang W, Cheng Y-P, Wang H-F, Li W, Wang L (2015) Coupled disaster-causing mechanisms of strata pressure behavior and abnormal gas emissions in underground coal extraction. Environ Earth Sci 74(9):6717\u20136735","journal-title":"Environ Earth Sci"},{"issue":"4","key":"1650_CR6","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1007\/s10706-018-00776-y","volume":"37","author":"S Gu","year":"2019","unstructured":"Gu S, Zhang W, Jiang B, Hu C (2019) Case of rock burst danger and its prediction and prevention in tunneling and mining period at an irregular coal face. Geotech Geol Eng 37(4):2545\u20132564","journal-title":"Geotech Geol Eng"},{"key":"1650_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.ijrmms.2018.12.010","volume":"114","author":"S Zhang","year":"2019","unstructured":"Zhang S, Li Y, Shen B, Sun X, Gao L (2019) Effective evaluation of pressure relief drilling for reducing rock bursts and its application in underground coal mines. Int J Rock Mech Min Sci 114:7\u201316","journal-title":"Int J Rock Mech Min Sci"},{"key":"1650_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.isatra.2021.04.021","volume":"122","author":"X Meng","year":"2022","unstructured":"Meng X, Yu H, Zhang J, Xu T, Wu H, Yan K (2022) Disturbance observer-based feedback linearization control for a quadruple-tank liquid level system. ISA Trans 122:146\u2013162","journal-title":"ISA Trans"},{"key":"1650_CR9","doi-asserted-by":"crossref","unstructured":"Telichenko, V., Rimshin, V., Eremeev, V., Kurbatov, V.: Mathematical modeling of groundwaters pressure distribution in the underground structures by cylindrical form zone. In: MATEC Web of Conferences, vol. 196, p. 02025 (2018). EDP Sciences","DOI":"10.1051\/matecconf\/201819602025"},{"issue":"1","key":"1650_CR10","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1080\/19475705.2020.1785956","volume":"11","author":"Z Li","year":"2020","unstructured":"Li Z, Wang Y, Olgun CG, Yang S, Jiao Q, Wang M (2020) Risk assessment of water inrush caused by karst cave in tunnels based on reliability and ga-bp neural network. Geomat Nat Haz Risk 11(1):1212\u20131232","journal-title":"Geomat Nat Haz Risk"},{"key":"1650_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115006","volume":"178","author":"DR Harp","year":"2021","unstructured":"Harp DR, O\u2019Malley D, Yan B, Pawar R (2021) On the feasibility of using physics-informed machine learning for underground reservoir pressure management. Exp Syst Appl 178:115006","journal-title":"Exp Syst Appl"},{"key":"1650_CR12","doi-asserted-by":"crossref","unstructured":"Radke D, Hessler A, Ellsworth D (2019) Firecast: leveraging deep learning to predict wildfire spread. In: IJCAI, pp 4575\u20134581","DOI":"10.24963\/ijcai.2019\/636"},{"issue":"6425","key":"1650_CR13","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1126\/science.aav7274","volume":"363","author":"RB Alley","year":"2019","unstructured":"Alley RB, Emanuel KA, Zhang F (2019) Advances in weather prediction. Science 363(6425):342\u2013344","journal-title":"Science"},{"key":"1650_CR14","doi-asserted-by":"crossref","unstructured":"Huang R, Wei C, Wang B, Yang J, Xu X, Wu S, Huang S (2021) Well performance prediction based on long short-term memory (lstm) neural network. J Petrol Sci Eng 109686","DOI":"10.1016\/j.petrol.2021.109686"},{"key":"1650_CR15","doi-asserted-by":"crossref","unstructured":"Kumar N, Kumar H (2021) A novel hybrid fuzzy time series model for prediction of covid-19 infected cases and deaths in India. ISA transactions","DOI":"10.1016\/j.isatra.2021.07.003"},{"issue":"1","key":"1650_CR16","doi-asserted-by":"publisher","first-page":"9253","DOI":"10.1126\/sciadv.aaw9253","volume":"6","author":"KA Reed","year":"2020","unstructured":"Reed KA, Stansfield A, Wehner M, Zarzycki C (2020) Forecasted attribution of the human influence on hurricane florence. Sci Adv 6(1):9253","journal-title":"Sci Adv"},{"key":"1650_CR17","unstructured":"Sontakke SA, Mehrjou A, Itti L, Sch\u00f6lkopf B (2021) Causal curiosity: Rl agents discovering self-supervised experiments for causal representation learning. In: International conference on machine learning, pp 9848\u20139858"},{"key":"1650_CR18","unstructured":"Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2000) Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 753\u2013763"},{"issue":"1","key":"1650_CR19","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1):4\u201324","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"1650_CR20","doi-asserted-by":"crossref","unstructured":"Peng, H., Zhang, R., Li, S., Cao, Y., Pan, S., Yu, P.: Reinforced, incremental and cross-lingual event detection from social messages. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)","DOI":"10.1109\/TPAMI.2022.3144993"},{"issue":"2194","key":"1650_CR21","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim B, Zohren S (2021) Time-series forecasting with deep learning: a survey. Phil Trans R Soc A 379(2194):20200209","journal-title":"Phil Trans R Soc A"},{"key":"1650_CR22","doi-asserted-by":"crossref","unstructured":"Wang J, Li Z, Long Q, Zhang W, Song G, Shi C (2020) Learning node representations from noisy graph structures. In: 2020 IEEE international conference on data mining (ICDM), pp 1310\u20131315, IEEE","DOI":"10.1109\/ICDM50108.2020.00169"},{"key":"1650_CR23","unstructured":"Wei WW (2006) Time series analysis. In: The Oxford handbook of quantitative methods in psychology: Vol 2"},{"key":"1650_CR24","doi-asserted-by":"crossref","unstructured":"Deng A, Hooi B (2021) Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4027\u20134035","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"1650_CR25","unstructured":"Zhu Y, Xu W, Zhang J, Liu Q, Wu S, Wang L (2021) Deep graph structure learning for robust representations: a survey. arXiv:2103.03036"},{"key":"1650_CR26","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ins.2021.07.007","volume":"578","author":"H Peng","year":"2021","unstructured":"Peng H, Du B, Liu M, Liu M, Ji S, Wang S, Zhang X, He L (2021) Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf Sci 578:401\u2013416","journal-title":"Inf Sci"},{"key":"1650_CR27","doi-asserted-by":"crossref","unstructured":"Peng H, Li J, Gong Q, Ning Y, Wang S, He L (2020) Motif-matching based subgraph-level attentional convolutional network for graph classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 5387\u20135394","DOI":"10.1609\/aaai.v34i04.5987"},{"key":"1650_CR28","unstructured":"Xu H, Huang Y, Duan Z, Feng J, Song P (2020) Multivariate time series forecasting based on causal inference with transfer entropy and graph neural network. arXiv:2005.01185"},{"key":"1650_CR29","doi-asserted-by":"crossref","unstructured":"Jin D, Huo C, Liang C, Yang L (2021) Heterogeneous graph neural network via attribute completion. In: Proceedings of the web conference 2021, pp 391\u2013400","DOI":"10.1145\/3442381.3449914"},{"key":"1650_CR30","unstructured":"Abu-El-Haija S, Perozzi B, Kapoor A, Alipourfard N, Lerman K, Harutyunyan H, Ver\u00a0Steeg G, Galstyan A (2019) Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: International Conference on Machine Learning, pp 21\u201329, PMLR"},{"issue":"4","key":"1650_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3490181","volume":"40","author":"H Peng","year":"2021","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Zhang J, Yu PS (2021) Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans Inf Syst (TOIS) 40(4):1\u201346","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"1650_CR32","unstructured":"Wang L (2018) Law and control of strata behavior in fully mechanized working face with shallow depth and high-intensity mining in Yushen mining area. PhD thesis, China University of Mining"},{"key":"1650_CR33","unstructured":"Tong Z, Yixin Z, Guangpei Z, Shaolei W, Zhenhua J (2016) A multi-coupling analysis of mining-induced pressure characteristics of shallow-depth coal face in shendong mining area. J Chin Coal Soc (S2):287\u2013296"},{"key":"1650_CR34","doi-asserted-by":"crossref","unstructured":"Peng H, Li J, Wang Z, Yang R, Liu M, Zhang M, Yu P, He L (2021) Lifelong property price prediction: a case study for the toronto real estate market. IEEE Trans Knowl Data Eng (2021)","DOI":"10.1109\/TKDE.2021.3112749"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01650-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01650-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01650-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:15:52Z","timestamp":1744204552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01650-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1650"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01650-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]},"assertion":[{"value":"13 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}