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Algorithms 2012, 5(3), 330-363; doi:10.3390/a5030330

Use of Logistic Regression for Forecasting Short-Term Volcanic Activity

1,* , 1
Received: 15 May 2012 / Revised: 28 July 2012 / Accepted: 7 August 2012 / Published: 22 August 2012
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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.
Keywords: logistic regression; eruption forecasting; event tree logistic regression; eruption forecasting; event tree
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Junek, W.N.; Jones, L.W.; Woods, M.T. Use of Logistic Regression for Forecasting Short-Term Volcanic Activity. Algorithms 2012, 5, 330-363.

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