## 5 Summary and Conclusions  
An algorithm that forecasts volcanic activity using an event tree analysis system and logistic regression has been developed, characterized, and validated. The suite of logistic models that drive the system were derived from a geographically diverse dataset comprised of source modeling results, monitoring data, and the historic behavior of analog volcanoes. This allows the algorithm to simultaneously utilize a diverse set of information in its decision making process. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation analysis produced a series of ROC curves with AUROC values in the 0.78-0.81 range, indicating that the algorithm has good forecasting capabilities. In addition, ROC curves also allowed for the determination of the false positive rate and optimum detection threshold for each stage of the algorithm. Modeling results had a significant influence on the probability estimates for the intrusion and eruption nodes and a moderate effect on the intensity node. Logistic functions illustrated in Figures 2-4 show the influence of modeling information on probabilities decreases between nodes 2, 3, and 4. This result makes sense, because the presence of fluid motion alone does not guarantee escalating volcanic activity. Probability estimates for node 2 should be significantly higher with positive modeling result, since its function is to forecast the presence of fluid motion. However, there is no guarantee an eruption will occur in the presence of fluid motion. Furthermore, the intensity of an eruption is a function of the fluid's chemical composition, accumulation rate, and amount pressure exerted on its storage vessel. Thus the degradation of the modeling results influence on the probability estimates generated by later nodes is consistent with eruption mechanics. A comparison of the performance between the VEFA and BETEF further illustrates the power of using source modeling information to produce short-term intrusion and eruption forecasts. Source modeling data significantly enhanced the VEFA's forecasting capabilities, especially in situations where little or no associated seismicity exists. This point is confirmed by the differences in the BETEF and VEFA intrusion and eruption forecasts for Okmok and Mount Saint Helens. In both cases, the VEFA leveraged source modeling information to confirm or deny the observed unrest was the result of fluid motion. While the BETEF was also given this information during the development and evaluation of its statistical models, it was unable to make the physical connection between the input data and the source of the unrest. Comparison results suggest that a static suite of empirical statistical models derived from a geographically diverse and sparse dataset are transportable and can compete with, and in some cases outperform, non-transportable empirical models trained exclusively from site specific information. This assessment is supported by comparison results showing that the VEFA forecasts are more consistent with the actual outcome of the test events listed in Table 5.  
To the best of our knowledge, this is the first time logistic regression is used to forecast volcanic activity. Furthermore, the derivation of optimized detection thresholds allowed for the quantification of the USGS hazard levels and the determination of an associated false positive rate. This was made possible by the data contained in the volcanic unrest database constructed exclusively for this research. The incorporation of source modeling data into the event tree's decision making process has initiated thetransition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based system. This paper shows the VEFA has potential for forecasting volcanic activity at various locations throughout the world.