[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122051-en":3,"doc-seo-122051-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122051,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning for Tsunami Waves Forecasting Using Regression Trees - research paper","After a seismic event, tsunami early warning systems (TEWSs) aim to forecast the maximum height of incident waves at coastal target points so that alerts can be issued where impacts may be destructive. Forecast uncertainty can be quantified through ensembles, and probabilistic tsunami hazard analysis relies on many simulations to reflect source variability. To improve accuracy and reduce computation, this work proposes regression-tree based machine learning using pre-computed ensemble simulations, evaluated on the 2003 Zemmouri-Boumerdes earthquake data, achieving high accuracy while delivering fully explainable, interpretable models for trustable uncertainty exploration in early-warning scenarios.","Big Data Research 36 (2024) 100452  \nContents lists available at ScienceDirect  \nBig Data Research  \njournal [homepage: www.elsevier.com/locate/bdr](homepage: www.elsevier.com/locate/bdr)  \n| Machine Learning for Tsunami Waves Forecasting Using Regression Trees |  |  |  |\n| --- | --- | --- | --- |\n| Eugenio Cesario a,b,∗ , Salvatore Giampá b, Enrico Baglione c,e, Louise Cordrie c, Jacopo Selva d,c, Domenico Talia a,b\u003Cbr>a University of Calabria, Italy b DtoK Lab, Italy\u003Cbr>c Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Bologna, Bologna, Italy\u003Cbr>d Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Università degli Studi di Napoli ‘Federico II’, Naples, Italy e Department of Physics and Astronomy, University of Bologna, via Irnerio 46, 40126 Bologna, Italy |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Tsunami forecasting Machine learning Regression tree |  | After a seismic event, tsunami early warning systems (TEWSs) try to accurately forecast the maximum height of incident waves at specific target points in front of the coast, so that early warnings can be launched on locations where the impact of tsunami waves can be destructive to deliver aids in these locations in the immediate postevent management. The uncertainty on the forecast can be quantified with ensembles of alternative scenarios. Similarly, in probabilistic tsunami hazard analysis (PTHA) a large number of simulations is required to cover the natural variability of the source process in each location. To improve the accuracy and computational efficiency of tsunami forecasting methods, scientists have recently started to exploit machine learning techniques to process pre-computed simulation data. However, the approaches proposed in literature, mainly based on neural networks, suffer of high training time and limited model explainability. To overtake these issues, this paper describes a machine learning approach based on regression trees to model and forecast tsunami evolutions. The algorithm takes as input a set of simulations forming an ensemble that describes potential benefit regional impact of tsunami source scenarios in a given source area, and it provides predictive models to forecast the tsunami waves for other potential tsunami sources in the same area. The experimental evaluation, performed on the 2003 M6.8 Zemmouri-Boumerdes earthquake and tsunami simulation data, shows that regression trees achieve high forecasting accuracy. Moreover, they provide domain experts with fully-explainable and interpretable models, which are a valuable support for environmental scientists because they describe underlying rules and patterns behind the models and allow for an explicit inspection of their functioning. This can enable a full and trustable exploration of source uncertainty in tsunami early-warning and urgent computing scenarios, with large ensembles of computationally light tsunami simulations. |  |\n\n1. Introduction  \nTsunamis can be devastating events, potentially causing huge environmental destruction, losses of human lives and economic collapses. The vast majority of tsunamis are generated by submarine earthquakes, even though many other potential sources for large tsunamis are possible [1]. Tsunami early warning systems (TEWSs) play a fundamental role in managing the risk connected to tsunamis. In particular, after a seismic event that occurs near or under the sea, TEWSs try to accurately forecast the maximum height of incident waves at specific target points in front of the coast. This information, in fact, is crucial to launch early warnings on locations where the impact of tsunami waves can be dan-  \n* Corresponding author at: University of Calabria, Italy. E-mail address: [eugenio.cesario@unical.it](eugenio.cesario@unical.it) (E. Cesario).  \ngerous (or even destructive), so it is really important that such systems provide their forecasts with short computation time while maintaining a high predictio","cbCaigoHS3so1gPi","https://ap.wps.com/l/cbCaigoHS3so1gPi","pdf",2498933,1,14,"English","en",105,"# Introduction\n## Tsunami early warning systems and forecasting needs\n## Uncertainty management and probabilistic analysis\n## Motivation for machine learning in tsunami forecasting\n## Contribution: regression trees for explainable forecasts","[{\"question\":\"What is the goal of tsunami early warning systems (TEWSs) described in the document?\",\"answer\":\"TEWSs forecast the maximum incident wave height at coastal target points to enable timely early warnings where tsunami impacts could be dangerous or destructive.\"},{\"question\":\"Why is uncertainty quantification important for tsunami forecasting and hazard analysis?\",\"answer\":\"Forecast uncertainty is addressed using ensembles of alternative scenarios, and probabilistic tsunami hazard analysis requires many simulations to cover natural variability in the source process.\"},{\"question\":\"How does the proposed regression-tree approach improve tsunami wave forecasting?\",\"answer\":\"It uses pre-computed ensemble simulation inputs to build predictive regression-tree models that forecast tsunami waves for other potential sources, achieving high accuracy with fully explainable and interpretable outputs.\"}]","Machine Learning for Tsunami Waves Forecasting Using Regression Trees - research paper | PDF",1785808575,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-tsunami-waves-forecasting-using-regression-trees-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-tsunami-waves-forecasting-using-regression-trees-research-paper/122051/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of tsunami early warning systems (TEWSs) described in the document?","Question",{"text":75,"@type":76},"TEWSs forecast the maximum incident wave height at coastal target points to enable timely early warnings where tsunami impacts could be dangerous or destructive.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is uncertainty quantification important for tsunami forecasting and hazard analysis?",{"text":80,"@type":76},"Forecast uncertainty is addressed using ensembles of alternative scenarios, and probabilistic tsunami hazard analysis requires many simulations to cover natural variability in the source process.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed regression-tree approach improve tsunami wave forecasting?",{"text":84,"@type":76},"It uses pre-computed ensemble simulation inputs to build predictive regression-tree models that forecast tsunami waves for other potential sources, achieving high accuracy with fully explainable and interpretable outputs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]