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- Title
Use of Logistic Regression for Forecasting Short-Term Volcanic Activity.
- Authors
Junek, William N.; Jones, W. Linwood; Woods, Mark T.
- Abstract
An algorithm that forecasts volcanic activity using an event tree decision making framework and logistic regression has been developed, characterized, and validated. The suite of empirical models that drive the system were derived from a sparse and geographically diverse dataset comprised of source modeling results, volcano monitoring data, and historic information from analog volcanoes. Bootstrapping techniques were applied to the training dataset to allow for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and rising eruption frequency. Cross validation yielded a series of receiver operating characteristic curves with areas ranging between 0.78 and 0.81, indicating that the algorithm has good forecasting capabilities. Our results suggest that the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information.
- Subjects
VOLCANIC activity prediction; LOGISTIC regression analysis; ALGORITHMS; VOLCANIC eruptions; VOLCANOES
- Publication
Algorithms, 2012, Vol 5, Issue 3, p330
- ISSN
1999-4893
- Publication type
Article
- DOI
10.3390/a5030330