{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:05:38Z","timestamp":1766729138346,"version":"3.48.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s12145-025-01764-6","type":"journal-article","created":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T05:48:41Z","timestamp":1742017721000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating nature-inspired refined red-tailed hawk algorithm with enhanced time-series dense encoder for seismic event prediction"],"prefix":"10.1007","volume":"18","author":[{"given":"Priyanka","family":"Kumari","sequence":"first","affiliation":[]},{"given":"Sunil","family":"kumar","sequence":"additional","affiliation":[]},{"given":"Ram Kumar","family":"Giri","sequence":"additional","affiliation":[]},{"given":"Sanju Kumari","family":"Sheshma","sequence":"additional","affiliation":[]},{"given":"Bhupesh Kumar","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,15]]},"reference":[{"issue":"11","key":"1764_CR1","doi-asserted-by":"publisher","first-page":"8412","DOI":"10.1109\/JIOT.2021.3114420","volume":"9","author":"MS Abdalzaher","year":"2021","unstructured":"Abdalzaher MS, Soliman MS, El-Hady SM, Benslimane A, Elwekeil M (2021) A deep learning model for earthquake parameters observation in IoT system-based earthquake early warning. IEEE Internet Things J 9(11):8412\u20138424","journal-title":"IEEE Internet Things J"},{"key":"1764_CR2","doi-asserted-by":"publisher","first-page":"107408","DOI":"10.1016\/j.cie.2021.107408","volume":"158","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408","journal-title":"Comput Ind Eng"},{"issue":"1","key":"1764_CR3","first-page":"2079","volume":"14","author":"OJ Abdulelah","year":"2023","unstructured":"Abdulelah OJ, Naimi S (2023) Seismic data analysis using feed forward BP neural network model for earthquake prediction. Int J Nonlinear Anal Appl 14(1):2079\u20132090","journal-title":"Int J Nonlinear Anal Appl"},{"issue":"2","key":"1764_CR4","doi-asserted-by":"publisher","first-page":"e2021JB023254","DOI":"10.1029\/2021JB023254","volume":"127","author":"F Aden-Antoni\u00f3w","year":"2022","unstructured":"Aden-Antoni\u00f3w F, Frank WB, Seydoux L (2022) An adaptable random forest model for the declustering of earthquake catalogs. J Geophys Res Solid Earth 127(2):e2021JB023254","journal-title":"J Geophys Res Solid Earth"},{"issue":"1","key":"1764_CR5","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10462-022-10173-w","volume":"56","author":"M Azizi","year":"2023","unstructured":"Azizi M, Talatahari S, Gandomi AH (2023) Fire Hawk Optimizer: A novel metaheuristic algorithm. Artif Intell Rev 56(1):287\u2013363","journal-title":"Artif Intell Rev"},{"issue":"2","key":"1764_CR6","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s10712-022-09698-0","volume":"43","author":"S Baranov","year":"2022","unstructured":"Baranov S, Narteau C, Shebalin P (2022) Modeling and prediction of aftershock activity. Surv Geophys 43(2):437\u2013481","journal-title":"Surv Geophys"},{"key":"1764_CR7","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.worlddev.2017.12.002","volume":"104","author":"P Brown","year":"2018","unstructured":"Brown P, Daigneault AJ, Tjernstr\u00f6m E, Zou W (2018) Natural disasters, social protection, and risk perceptions. World Dev 104:310\u2013325","journal-title":"World Dev"},{"issue":"2","key":"1764_CR8","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s11069-021-05106-9","volume":"111","author":"HJ Caldera","year":"2022","unstructured":"Caldera HJ, Wirasinghe SC (2022) A universal severity classification for natural disasters. Nat Hazards 111(2):1533\u20131573","journal-title":"Nat Hazards"},{"key":"1764_CR9","doi-asserted-by":"publisher","first-page":"102329","DOI":"10.1016\/j.gloenvcha.2021.102329","volume":"70","author":"F Cappelli","year":"2021","unstructured":"Cappelli F, Costantini V, Consoli D (2021) The trap of climate change-induced \u201cnatural\u201d disasters and inequality. Glob Environ Chang 70:102329","journal-title":"Glob Environ Chang"},{"key":"1764_CR10","doi-asserted-by":"crossref","unstructured":"Cesario E, Giamp\u00e1 S, Baglione E, Cordrie L, Selva J, Talia D (2024) Machine Learning for Tsunami Waves Forecasting Using Regression Trees. Big Data Res 100452","DOI":"10.1016\/j.bdr.2024.100452"},{"key":"1764_CR11","doi-asserted-by":"crossref","unstructured":"Chittora P, Chakrabarti T, Debnath P, Gupta A, Chakrabarti P, Praveen S, Margala M, Elngar AA (2022) Experimental analysis of earthquake prediction using machine learning classifiers, curve fitting, and neural modeling.","DOI":"10.21203\/rs.3.rs-1896823\/v2"},{"key":"1764_CR12","unstructured":"Das A, Kong W, Leach A, Sen R, Yu R (2023) Long-term Forecasting with TiDE: Time-series Dense Encoder. arXiv preprint arXiv:2304.08424."},{"issue":"6","key":"1764_CR13","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1007\/s10712-020-09608-2","volume":"41","author":"JR Elliott","year":"2020","unstructured":"Elliott JR, de Michele M, Gupta HK (2020) Earth observation for crustal tectonics and earthquake hazards. Surv Geophys 41(6):1355\u20131389","journal-title":"Surv Geophys"},{"issue":"1","key":"1764_CR14","doi-asserted-by":"publisher","first-page":"12950","DOI":"10.1038\/s41598-023-38778-3","volume":"13","author":"S Ferahtia","year":"2023","unstructured":"Ferahtia S, Houari A, Rezk H, Djerioui A, Machmoum M, Motahhir S, Ait-Ahmed M (2023) Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep 13(1):12950","journal-title":"Sci Rep"},{"issue":"7","key":"1764_CR15","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1029\/2017GC007391","volume":"19","author":"SE Graham","year":"2018","unstructured":"Graham SE, Loveless JP, Meade BJ (2018) Global plate motions and earthquake cycle effects. Geochem Geophys Geosyst 19(7):2032\u20132048","journal-title":"Geochem Geophys Geosyst"},{"key":"1764_CR16","doi-asserted-by":"publisher","first-page":"107889","DOI":"10.1016\/j.geomorph.2021.107889","volume":"391","author":"Q He","year":"2021","unstructured":"He Q, Wang M, Liu K (2021) Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest. Geomorphology 391:107889","journal-title":"Geomorphology"},{"issue":"1","key":"1764_CR17","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s44285-024-00013-4","volume":"2","author":"S He","year":"2024","unstructured":"He S, Liao Y, Sun PP, Zhang R (2024) Deep learning enabled seismic fragility evaluation of structures subjected to mainshock-aftershock earthquakes. Urban Lifeline 2(1):2","journal-title":"Urban Lifeline"},{"key":"1764_CR18","doi-asserted-by":"crossref","unstructured":"Hirshorn B, Weinstein S, Wang D, Koyanagi K, Becker N, McCreery C (2021) Earthquake Source Parameters: Rapid Estimates for Tsunami Forecasts and Warnings. In\u00a0Complexity in Tsunamis, Volcanoes, and their Hazards\u00a0(pp. 299\u2013333). New York, NY: Springer US.","DOI":"10.1007\/978-1-0716-1705-2_160"},{"issue":"1","key":"1764_CR19","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11227-024-06664-y","volume":"81","author":"XN Li","year":"2025","unstructured":"Li XN, Chen FJ, Lai YP, Tang P, Liang XJ (2025) ICAT-net: a lightweight neural network with optimized coordinate attention and transformer mechanisms for earthquake detection and phase picking. J Supercomput 81(1):191","journal-title":"J Supercomput"},{"key":"1764_CR20","doi-asserted-by":"crossref","unstructured":"Liu Y, Dong H, Wang X, Han S (2019) Time series prediction based on temporal convolutional network. In 2019 IEEE\/ACIS 18th International conference on computer and information science (ICIS) (pp. 300\u2013305). IEEE.","DOI":"10.1109\/ICIS46139.2019.8940265"},{"key":"1764_CR21","first-page":"84","volume":"4","author":"H Mai","year":"2023","unstructured":"Mai H, Audet P, Perry HC, Mousavi SM, Zhang Q (2023) Blockly earthquake transformer: A deep learning platform for custom phase picking. Artif Intell Geosci 4:84\u201394","journal-title":"Artif Intell Geosci"},{"key":"1764_CR22","doi-asserted-by":"publisher","first-page":"100113","DOI":"10.1016\/j.pdisas.2020.100113","volume":"7","author":"F Makinoshima","year":"2020","unstructured":"Makinoshima F, Imamura F, Oishi Y (2020) Tsunami evacuation processes based on human behaviour in past earthquakes and tsunamis: A literature review. Prog Disaster Sci 7:100113","journal-title":"Prog Disaster Sci"},{"issue":"1","key":"1764_CR23","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.1038\/s41467-021-22348-0","volume":"12","author":"F Makinoshima","year":"2021","unstructured":"Makinoshima F, Oishi Y, Yamazaki T, Furumura T, Imamura F (2021) Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks. Nat Commun 12(1):2253","journal-title":"Nat Commun"},{"key":"1764_CR24","doi-asserted-by":"publisher","first-page":"107050","DOI":"10.1016\/j.cie.2020.107050","volume":"152","author":"A Mohammadi-Balani","year":"2021","unstructured":"Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050","journal-title":"Comput Ind Eng"},{"issue":"1","key":"1764_CR25","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1093\/gji\/ggaa609","volume":"225","author":"J M\u00fcnchmeyer","year":"2021","unstructured":"M\u00fcnchmeyer J, Bindi D, Leser U, Tilmann F (2021) The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophys J Int 225(1):646\u2013656","journal-title":"Geophys J Int"},{"key":"1764_CR26","doi-asserted-by":"crossref","unstructured":"Murshed RU, Noshin K, Zakaria MA, Uddin MF, Amin AS, Ali ME (2024) Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning.\u00a0IEEE Transactions on Geoscience and Remote Sensing.","DOI":"10.1109\/TGRS.2024.3373643"},{"issue":"4","key":"1764_CR27","doi-asserted-by":"publisher","first-page":"2320","DOI":"10.1785\/0220200004","volume":"91","author":"JS Neely","year":"2020","unstructured":"Neely JS, Stein S, Spencer BD (2020) Large uncertainties in earthquake stress-drop estimates and their tectonic consequences. Seismol Res Lett 91(4):2320\u20132329","journal-title":"Seismol Res Lett"},{"key":"1764_CR28","unstructured":"Oreshkin BN, Carpov D, Chapados N, Bengio Y (2019) N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437."},{"issue":"1","key":"1764_CR29","doi-asserted-by":"publisher","first-page":"100130","DOI":"10.1016\/j.geogeo.2022.100130","volume":"2","author":"SC Pal","year":"2023","unstructured":"Pal SC, Saha A, Chowdhuri I, Ruidas D, Chakrabortty R, Roy P, Shit M (2023) Earthquake hotspot and coldspot: Where, why and how? Geosyst Geoenviron 2(1):100130","journal-title":"Geosyst Geoenviron"},{"issue":"18","key":"1764_CR30","doi-asserted-by":"publisher","first-page":"11691","DOI":"10.1007\/s00521-021-05872-4","volume":"33","author":"Y Pu","year":"2021","unstructured":"Pu Y, Chen J, Apel DB (2021) Deep and confident prediction for a laboratory earthquake. Neural Comput Appl 33(18):11691\u201311701","journal-title":"Neural Comput Appl"},{"key":"1764_CR31","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1007\/s00024-020-02483-3","volume":"177","author":"RN Ratnasari","year":"2020","unstructured":"Ratnasari RN, Tanioka Y, Gusman AR (2020) Determination of source models appropriate for tsunami forecasting: application to tsunami earthquakes in central Sumatra, Indonesia. Pure Appl Geophys 177:2551\u20132562","journal-title":"Pure Appl Geophys"},{"issue":"5","key":"1764_CR32","first-page":"654","volume":"12","author":"MA Salam","year":"2021","unstructured":"Salam MA, Ibrahim L, Abdelminaam DS (2021) Earthquake prediction using hybrid machine learning techniques. Int J Adv Comput Sci Appl 12(5):654\u20136652021","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"4","key":"1764_CR33","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1007\/s00366-022-01604-x","volume":"39","author":"A Seyyedabbasi","year":"2023","unstructured":"Seyyedabbasi A, Kiani F (2023) Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Eng Comput 39(4):2627\u20132651","journal-title":"Eng Comput"},{"issue":"1","key":"1764_CR34","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s10596-022-10187-x","volume":"27","author":"C ShajuKamal","year":"2023","unstructured":"ShajuKamal C (2023) Analysis of earthquake hypocenter characteristics using chaos game representation. Comput Geosci 27(1):143\u2013157","journal-title":"Comput Geosci"},{"key":"1764_CR35","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.quaint.2021.11.006","volume":"651","author":"PG Silva","year":"2023","unstructured":"Silva PG, Elez J, P\u00e9rez-L\u00f3pez R, Giner-Robles JL, G\u00f3mez-Diego PV, Roquero E, Rodr\u00edguez-Pascua M\u00c1, Bardaj\u00ed T (2023) The AD 1755 Lisbon Earthquake-Tsunami: Seismic source modelling from the analysis of ESI-07 environmental data. Quatern Int 651:6\u201324","journal-title":"Quatern Int"},{"issue":"2","key":"1764_CR36","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1093\/gji\/ggac220","volume":"231","author":"J Song","year":"2022","unstructured":"Song J, Zhu J, Wang Y, Li S (2022) On-site alert-level earthquake early warning using machine-learning-based prediction equations. Geophys J Int 231(2):786\u2013800","journal-title":"Geophys J Int"},{"key":"1764_CR37","unstructured":"Wang Y, Wu H, Dong J, Qin G, Zhang H, Liu Y, Qiu Y, Wang J, Long M (2024) Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072."},{"key":"1764_CR38","doi-asserted-by":"publisher","first-page":"48322","DOI":"10.1109\/ACCESS.2023.3276628","volume":"11","author":"X Wen","year":"2023","unstructured":"Wen X, Li W (2023) Time series prediction based on LSTM-attention-LSTM model. IEEE Access 11:48322\u201348331","journal-title":"IEEE Access"},{"issue":"11","key":"1764_CR39","doi-asserted-by":"publisher","first-page":"8691","DOI":"10.1016\/j.aej.2022.02.001","volume":"61","author":"M Xiang","year":"2022","unstructured":"Xiang M, Deng Q, Duan L, Yang J, Wang C, Liu J, Liu M (2022) Dynamic monitoring and analysis of the earthquake Worst-hit area based on remote sensing. Alex Eng J 61(11):8691\u20138702","journal-title":"Alex Eng J"},{"issue":"8","key":"1764_CR40","doi-asserted-by":"publisher","first-page":"e2022GL098545","DOI":"10.1029\/2022GL098545","volume":"49","author":"J Yang","year":"2022","unstructured":"Yang J, Xu C, Wen Y, Xu G (2022) Complex coseismic and postseismic faulting during the 2021 northern Thessaly (Greece) earthquake sequence illuminated by InSAR observations. Geophys Res Lett 49(8):e2022GL098545","journal-title":"Geophys Res Lett"},{"key":"1764_CR41","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s10950-021-09999-8","volume":"25","author":"R Yuan","year":"2021","unstructured":"Yuan R (2021) An improved K-means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction. J Seismolog 25:1005\u20131020","journal-title":"J Seismolog"},{"issue":"12","key":"1764_CR42","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/s40766-022-00038-x","volume":"45","author":"D Zaccagnino","year":"2022","unstructured":"Zaccagnino D, Doglioni C (2022) Earth\u2019s gradients as the engine of plate tectonics and earthquakes. La Rivista Del Nuovo Cimento 45(12):801\u2013881","journal-title":"La Rivista Del Nuovo Cimento"},{"key":"1764_CR43","first-page":"5915712","volume":"61","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Wang Y (2023) A Spatiotemporal Model for Global Earthquake Prediction Based on Convolutional LSTM. IEEE Trans Geosci Remote Sens 61:5915712","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1764_CR44","doi-asserted-by":"publisher","first-page":"121659","DOI":"10.1016\/j.ins.2024.121659","volume":"692","author":"F Zhang","year":"2025","unstructured":"Zhang F, Wang M, Zhang W, Wang H (2025) THATSN: Temporal hierarchical aggregation tree structure network for long-term time-series forecasting. Inf Sci 692:121659","journal-title":"Inf Sci"},{"key":"1764_CR45","doi-asserted-by":"publisher","first-page":"110985","DOI":"10.1016\/j.patcog.2024.110985","volume":"158","author":"D Zhang","year":"2025","unstructured":"Zhang D, Zhang Z, Chen N, Wang Y (2025) Dynamic convolutional time series forecasting based on adaptive temporal bilateral filtering. Pattern Recogn 158:110985","journal-title":"Pattern Recogn"},{"key":"1764_CR46","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.ijdrr.2017.09.037","volume":"27","author":"L Zhou","year":"2018","unstructured":"Zhou L, Wu X, Xu Z, Fujita H (2018) Emergency decision making for natural disasters: An overview. Int J Disaster Risk Reduction 27:567\u2013576","journal-title":"Int J Disaster Risk Reduction"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01764-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01764-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01764-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:00:39Z","timestamp":1766728839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01764-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,15]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["1764"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01764-6","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"type":"print","value":"1865-0473"},{"type":"electronic","value":"1865-0481"}],"subject":[],"published":{"date-parts":[[2025,3,15]]},"assertion":[{"value":"30 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants and\/or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"There is no informed consent for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"329"}}