Automated Tsunami Source Modeling Using the Sweeping Window Positive Elastic Net

Autor:	Edison Gica, Harold O. Mofjeld, Paul Y. Huang, Daniel M. Percival, Michael C. Spillane, Donald B. Percival, Donald W. Denbo
Rok vydání:	2014
Předmět:	Statistics and Probability Elastic net regularization Matching (statistics) Computer science Window (computing) Estimator Feature selection computer.software_genre Set (abstract data type) Epicenter Data mining Statistics Probability and Uncertainty Akaike information criterion computer
Zdroj:	Journal of the American Statistical Association. 109:491-499
ISSN:	1537-274X 0162-1459
Popis:	In response to hazards posed by earthquake-induced tsunamis, the National Oceanographic and Atmospheric Administration developed a system for issuing timely warnings to coastal communities. This system, in part, involves matching data collected in real time from deep-ocean buoys to a database of precomputed geophysical models, each associated with a geographical location. Currently, trained operators must handpick models from the database using the epicenter of the earthquake as guidance, which can delay issuing of warnings. In this article, we introduce an automatic procedure to select models to improve the timing and accuracy of these warnings. This procedure uses an elastic-net-based penalized and constrained linear least-squares estimator in conjunction with a sweeping window. This window ensures that selected models are close spatially, which is desirable from geophysical considerations. We use the Akaike information criterion to settle on a particular window and to set the tuning parameters associat...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0130599d307ca26571f63421ff73dddc https://doi.org/10.1080/01621459.2013.879062 Zobrazit plný text záznamu