## 1 Introduction  
Volcanism combines complex geophysical and geochemical processes that vary in composition, duration, and intensity from location to location and episode to episode. Therefore, successful eruption forecasting will only be achieved through the use of methods that simultaneously weigh empirical experience and real time interpretation of processes [1]. Event trees have proven to be useful tools for forecasting volcanic activity [2; 3; 4]. In 2002, [PERSON] and [PERSON] outlined a generic event tree structure for forecasting various types of volcanic activity and associated hazards. This process has since been adopted and improved upon by a variety of researchers to forecast volcanic activity at various locations throughout the world [5; 6; 7; 8; 9; 10; 11; 12]. These event tree implementations require probability density functions to be manually derived on a volcano-by-volcano basis. This means every volcano being monitored will have a unique and unvalidated set of statistical functions driving its prediction process. As a result, each instance of the algorithm will use subjectively selected detection thresholds that possess a unique and unquantified false alarm rate.  
This research initiative aims to improve and standardize the event tree forecasting process. The method employed here also uses Newhall and Hoblitt's generic event tree infrastructure. However, we have augmented its decision making process with a suite of empirical statistical models that are derived through logistic regression. Each model is constructed from a geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g., seismic), source modeling results, and historic eruption activity information. The regression uses a generalized linear model routine (GLMR) that assumes a binominal response variable and employs a logit linking function. This process allows for trends in the relationship between modeling results, monitoring data, historic information, and the known outcome of the events to drive the formulation of the statistical models. It yields a static set of logistic models that weight the contributions of empirical experience and monitoring data relative to one another. They estimate the probability of a particular event occurring based on the current values of a set of predefined explanatory variables, are highly transportable, and do not need to be redefined for each volcano being monitored. This provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes in short-term forecasts. In addition, a rigorous cross validation process is used to document the algorithm's performance, identify optimum detection thresholds, and quantify false alarm rates. The methodology is easily extensible, where recalibration can be performed or new branches added to the decision making process with relative ease. Such a system could aid federal, state, and local emergency management officials in determining the proper response to an impending eruption and can be easily deployed by a National Volcano Early Warning System (NVEWS).  
This paper is organized into several sections. It begins with an overview of the volcanic eruption forecasting algorithm, logistic models, and cross validation analysis. The next section provides a detailed discussion of forecasts generated by the algorithm. The final section discusses the conclusions that can be drawn from this work and provides an overview of future research initiatives that may improve the algorithm's performance.