Evaluating the predicted eruption times of geysers in Yellowstone National Park

TL;DR

This paper evaluates the accuracy of predicted eruption times of Yellowstone geysers, comparing official models with actual data through statistical analysis to assess their reliability for visitors and researchers.

Contribution

It provides an empirical assessment of the accuracy of existing geyser eruption time predictions using statistical regression and pattern analysis.

Findings

Predicted eruption times closely match actual data for Old Faithful.

Statistical models can effectively capture eruption time patterns.

Some discrepancies suggest room for model improvement.

Abstract

This study aims to evaluate the accuracy of predicted eruption times of popular geysers in the Yellowstone National Park. The Yellowstone National Park was the first national park in the United States and is known for its geothermal features consisting of many highly popular geysers such as the Old Faithful. Geysers are fascinating to national park visitors because their eruptions could range from small bubbles to jets of water that are hundreds of meters high, and their eruptions could last from seconds to hours. To help tourists plan their visits, the US National Park Service and other independent groups publish predicted eruption times of popular geysers. We hypothesized that the models developed by the US National Park Service are very accurate with little discrepancy from independent analysis, as park rangers monitor the geysers constantly and likely adjust their models over time according to changing conditions underground, and patterns observed. In addition, since researchers in the park likely rely on these predictions, the models would need to be fine-tuned to ensure that no unnecessary effort or resources are wasted in probing the geysers for variables such as temperature and acidity. In this study, we focused on the Old Faithful and Beehive Geyser by downloading actual eruption times, conducting statistical regression analyses, studying the patterns of eruption times, and evaluating the accuracy of different statistical models.