{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:12:28Z","timestamp":1790442748154,"version":"4.1.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Arastirma Kurumu","doi-asserted-by":"publisher","award":["T\u00dcBITAK 2210-A National Scholarship Program for MSc Students"],"award-info":[{"award-number":["T\u00dcBITAK 2210-A National Scholarship Program for MSc Students"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00521-022-07030-w","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T18:03:03Z","timestamp":1649872983000},"page":"11049-11062","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Structural recurrent neural network models for earthquake prediction"],"prefix":"10.1007","volume":"34","author":[{"given":"Ayd\u0131n","family":"Do\u011fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4792-6698","authenticated-orcid":false,"given":"Engin","family":"Demir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"issue":"5","key":"7030_CR1","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1785\/BSSA0680051521","volume":"68","author":"RV Allen","year":"1978","unstructured":"Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68(5):1521\u20131532","journal-title":"Bull Seismol Soc Am"},{"key":"7030_CR2","first-page":"51","volume":"117","author":"EI Alves","year":"1994","unstructured":"Alves EI (1994) Notice on the predictability of earthquake occurrences. Mem Not 117:51\u201361","journal-title":"Mem Not"},{"issue":"1","key":"7030_CR3","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s11069-016-2579-3","volume":"85","author":"K Asim","year":"2017","unstructured":"Asim K, Martinez-Alvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85(1):471\u2013486","journal-title":"Nat Hazards"},{"issue":"5","key":"7030_CR4","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s11600-017-0082-1","volume":"65","author":"KM Asim","year":"2017","unstructured":"Asim KM, Awais M, Martinez-Alvarez F, Iqbal T (2017) Seismic activity prediction using computational intelligence techniques in northern Pakistan. Acta Geophys 65(5):919\u2013930","journal-title":"Acta Geophys"},{"issue":"5","key":"7030_CR5","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1109\/JSTARS.2014.2321972","volume":"7","author":"W Astuti","year":"2014","unstructured":"Astuti W, Akmeliawati R, Sediono W, Salami MJE (2014) Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction. IEEE J Sel Top Appl Earth Observations Remote Sens 7(5):1719\u20131728","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"issue":"4","key":"7030_CR6","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1785\/BSSA0770041437","volume":"77","author":"M Baer","year":"1987","unstructured":"Baer M, Kradolfer U (1987) An automatic phase picker for local and teleseismic events. Bull Seismol Soc Am 77(4):1437\u20131445","journal-title":"Bull Seismol Soc Am"},{"key":"7030_CR7","doi-asserted-by":"publisher","first-page":"192880","DOI":"10.1109\/ACCESS.2020.3029859","volume":"8","author":"MHA Banna","year":"2020","unstructured":"Banna MHA, Taher KA, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, Cho GH (2020) Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access 8:192880\u2013192923","journal-title":"IEEE Access"},{"key":"7030_CR8","doi-asserted-by":"crossref","unstructured":"Biswas S, Gall J (2018) Structural recurrent neural network (SRNN) for group activity analysis. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1625\u20131632","DOI":"10.1109\/WACV.2018.00180"},{"issue":"3","key":"7030_CR9","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/S0264-3707(01)00039-4","volume":"32","author":"B Bodri","year":"2001","unstructured":"Bodri B (2001) A neural-network model for earthquake occurrence. J Geodyn 32(3):289\u2013310","journal-title":"J Geodyn"},{"issue":"7720","key":"7030_CR10","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1038\/s41586-018-0438-y","volume":"560","author":"PM DeVries","year":"2018","unstructured":"DeVries PM, Vi\u00e9gas F, Wattenberg M, Meade BJ (2018) Deep learning of aftershock patterns following large earthquakes. Nature 560(7720):632\u2013634","journal-title":"Nature"},{"key":"7030_CR11","doi-asserted-by":"crossref","unstructured":"Do\u011fan A, Demir E (2020) Earthquake prediction in Turkey with structural recurrent neural network. In: IEEE 28th signal processing and communications applications conference (SIU 2020)","DOI":"10.1109\/SIU49456.2020.9302400"},{"key":"7030_CR12","doi-asserted-by":"crossref","unstructured":"Fan J, Chen Z, Yan L, Gong J, Wang D (2015) Research on earthquake prediction from infrared cloud images. In: MIPPR 2015: remote sensing image processing, geographic information systems, and other applications, international society for optics and photonics, vol 9815, p 98150E","DOI":"10.1117\/12.2203657"},{"key":"7030_CR13","doi-asserted-by":"crossref","unstructured":"Fu X, Gao F, Wu J, Wei X, Duan F (2019) Spatiotemporal attention networks for wind power forecasting. In: 2019 international conference on data mining workshops (ICDMW), IEEE, pp 149\u2013154","DOI":"10.1109\/ICDMW.2019.00032"},{"issue":"3","key":"7030_CR14","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1111\/j.1365-246X.1997.tb06588.x","volume":"131","author":"RJ Geller","year":"1997","unstructured":"Geller RJ (1997) Earthquake prediction: a critical review. Geophys J Int 131(3):425\u2013450","journal-title":"Geophys J Int"},{"issue":"1","key":"7030_CR15","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1111\/j.1365-246X.2006.02865.x","volume":"165","author":"SJ Gibbons","year":"2006","unstructured":"Gibbons SJ, Ringdal F (2006) The detection of low magnitude seismic events using array-based waveform correlation. Geophys J Int 165(1):149\u2013166","journal-title":"Geophys J Int"},{"key":"7030_CR16","doi-asserted-by":"crossref","unstructured":"Hayakawa M (2016) Earthquake prediction with electromagnetic phenomena. In: AIP conference proceedings, AIP publishing LLC, vol 1709, p 020002","DOI":"10.1063\/1.4941201"},{"issue":"03","key":"7030_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.4236\/ojer.2016.53013","volume":"5","author":"M Hayakawa","year":"2016","unstructured":"Hayakawa M, Yamauchi H, Ohtani N, Ohta M, Tosa S, Asano T, Schekotov A, Izutsu J, Potirakis SM, Eftaxias K et al (2016) On the precursory abnormal animal behavior and electromagnetic effects for the Kobe earthquake ($$\\text{ M}^{\\sim }$$ 6) on april 12, 2013. Open J Earthq Res 5(03):165","journal-title":"Open J Earthq Res"},{"issue":"2","key":"7030_CR18","doi-asserted-by":"publisher","first-page":"149","DOI":"10.14311\/NNW.2018.28.009","volume":"28","author":"J Huang","year":"2018","unstructured":"Huang J, Wang X, Zhao Y, Xin C, Xiang H (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Netw World 28(2):149\u2013160","journal-title":"Neural Netw World"},{"key":"7030_CR19","doi-asserted-by":"crossref","unstructured":"Jain A, Zamir AR, Savarese S, Saxena A (2016) Structural-rnn: deep learning on spatio-temporal graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5308\u20135317","DOI":"10.1109\/CVPR.2016.573"},{"key":"7030_CR20","doi-asserted-by":"crossref","unstructured":"Jiang M (2016) Easily magnetic anomalies earthquake prediction. In: MATEC web of conferences, EDP sciences, vol 63, p 01020","DOI":"10.1051\/matecconf\/20166301020"},{"key":"7030_CR21","unstructured":"Kail R, Zaytsev A, Burnaev E (2020) Recurrent convolutional neural networks help to predict location of earthquakes. arXiv preprint arXiv:2004.09140"},{"key":"7030_CR22","doi-asserted-by":"crossref","unstructured":"Kim Y, Wang P, Mihaylova L (2019) Structural recurrent neural network for traffic speed prediction. In: ICASSP 2019\u20132019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 5207\u20135211","DOI":"10.1109\/ICASSP.2019.8683670"},{"issue":"7553","key":"7030_CR23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"7030_CR24","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang Y, Li Y, Zhang B, Wu G (2004) Earthquake prediction by RBF neural network ensemble. In: International symposium on neural networks, Springer, pp 962\u2013969","DOI":"10.1007\/978-3-540-28648-6_153"},{"issue":"3","key":"7030_CR25","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1785\/gssrl.83.3.531","volume":"83","author":"A Lomax","year":"2012","unstructured":"Lomax A, Satriano C, Vassallo M (2012) Automatic picker developments and optimization: Filterpicker-a robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismol Res Lett 83(3):531\u2013540","journal-title":"Seismol Res Lett"},{"key":"7030_CR26","doi-asserted-by":"crossref","unstructured":"Mignan A, Broccardo M (2019) A deeper look into deep learning of aftershock patterns following large earthquakes: illustrating first principles in neural network physical interpretability. In: International work-conference on artificial neural networks, Springer, pp 3\u201314","DOI":"10.1007\/978-3-030-20521-8_1"},{"issue":"7776","key":"7030_CR27","doi-asserted-by":"publisher","first-page":"E1","DOI":"10.1038\/s41586-019-1582-8","volume":"574","author":"A Mignan","year":"2019","unstructured":"Mignan A, Broccardo M (2019) One neuron versus deep learning in aftershock prediction. Nature 574(7776):E1\u2013E3","journal-title":"Nature"},{"key":"7030_CR28","doi-asserted-by":"crossref","unstructured":"Miller HJ (2004) Tobler\u2019s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284\u2013289, http:\/\/www.jstor.org\/stable\/3693985","DOI":"10.1111\/j.1467-8306.2004.09402005.x"},{"issue":"1","key":"7030_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17591-w","volume":"11","author":"SM Mousavi","year":"2020","unstructured":"Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11(1):1\u201312","journal-title":"Nat Commun"},{"issue":"01","key":"7030_CR30","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1142\/S0129065707000890","volume":"17","author":"A Panakkat","year":"2007","unstructured":"Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17(01):13\u201333","journal-title":"Int J Neural Syst"},{"issue":"4","key":"7030_CR31","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1111\/j.1467-8667.2009.00595.x","volume":"24","author":"A Panakkat","year":"2009","unstructured":"Panakkat A, Adeli H (2009) Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput Aided Civ Infrastruct Eng 24(4):280\u2013292","journal-title":"Comput Aided Civ Infrastruct Eng"},{"issue":"2","key":"7030_CR32","doi-asserted-by":"publisher","first-page":"e1700578","DOI":"10.1126\/sciadv.1700578","volume":"4","author":"T Perol","year":"2018","unstructured":"Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):e1700578","journal-title":"Sci Adv"},{"key":"7030_CR33","doi-asserted-by":"crossref","unstructured":"Rasel RI, Sultana N, Islam GA, Islam M, Meesad P (2019) Spatio-temporal seismic data analysis for predicting earthquake: Bangladesh perspective. In: 2019 research, invention, and innovation congress (RI2C), IEEE, pp 1\u20135","DOI":"10.1109\/RI2C48728.2019.8999880"},{"issue":"1","key":"7030_CR34","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1029\/2018JB016674","volume":"124","author":"ZE Ross","year":"2019","unstructured":"Ross ZE, Yue Y, Meier MA, Hauksson E, Heaton TH (2019) Phaselink: a deep learning approach to seismic phase association. J Geophys Res Solid Earth 124(1):856\u2013869","journal-title":"J Geophys Res Solid Earth"},{"issue":"5","key":"7030_CR35","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.5194\/nhess-15-1061-2015","volume":"15","author":"J Thomas","year":"2015","unstructured":"Thomas J, Masci F, Love JJ et al (2015) On a report that the 2012 M6: 0 earthquake in Italy was predicted after seeing an unusual cloud formation. Nat Hazards Earth Syst Sci (NHESS) 15(5):1061\u20131068","journal-title":"Nat Hazards Earth Syst Sci (NHESS)"},{"key":"7030_CR36","doi-asserted-by":"crossref","unstructured":"Vemula A, Muelling K, Oh J (2018) Social attention: modeling attention in human crowds. In: 2018 IEEE international conference on robotics and automation (ICRA), IEEE, pp 1\u20137","DOI":"10.1109\/ICRA.2018.8460504"},{"issue":"1","key":"7030_CR37","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TETC.2017.2699169","volume":"8","author":"Q Wang","year":"2020","unstructured":"Wang Q, Guo Y, Yu L, Li P (2020) Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Trans Emerg Top Comput 8(1):148\u2013158. https:\/\/doi.org\/10.1109\/TETC.2017.2699169","journal-title":"IEEE Trans Emerg Top Comput"},{"key":"7030_CR38","doi-asserted-by":"crossref","unstructured":"Yoon CE, O\u2019Reilly O, Bergen KJ, Beroza GC (2015) Earthquake detection through computationally efficient similarity search. Sci Adv 1(11):e1501057","DOI":"10.1126\/sciadv.1501057"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07030-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07030-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07030-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T09:21:56Z","timestamp":1656148916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07030-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,13]]},"references-count":38,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["7030"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07030-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,13]]},"assertion":[{"value":"7 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no other conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}