Title

Use Of Logistic Regression For Forecasting Short-Term Volcanic Activity

Keywords

Eruption forecasting; Event tree; Logistic regression

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 thatthe 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. © 2012 by the authors.

Publication Date

10-30-2012

Publication Title

Algorithms

Volume

5

Issue

3

Number of Pages

330-363

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3390/a5030330

Socpus ID

84867884834 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84867884834

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