Javalanche: An Avalanche Predictor

This article introduces a prototypical avalanche-predicting software package implemented with a Fuzzy Logic algorithm.

Javalanche is prototypical in the sense that the current model is too sparse and naive for practical avalanche prediction. Nevertheless, it suggests that Fuzzy Logic may be an appropriate tool for such an application, upon significant enhancement of the model presented here. The software was developed using Java in a Debian/GNU Linux environment. Graphs were created using gnuplot.

Variables for Avalanche Prediction

Evaluating avalanche hazard relies on gathering meaningful data from a large number of variables including slope aspect and angle, wind load and direction, terrain roughness, snow crystal forms present in the snowpack, snowpack layer resistances, the layering effect of strong over weak zones, current temperature and temperature history, and recent snowfall depth and water content. It is noteworthy that both long-term and current variables belong in any usable model, that some factors are interrelated and that a factor may or may not play a predominant role at some particular time.

To be practical, the values of the input variables should be relatively straightforward to measure in environments ranging from tamed ski areas to untamed wilderness. Many of the typical assessment tools are qualitative but have proved their worth. Snow layers can be assessed by digging a snow pit and examining the pit walls for snow crystal forms, temperatures and layer resistances. A common method for assessing snow layer resistance is a hand test which measures the level of resistance the snow layer presents to penetration. These levels are categorized as fist, four finger, one finger, pencil and knife in order of increasing resistance. This aids in determining the existence of a buried instability. A technique for assessing the amount of surface snow that can be transported by wind is the foot penetration test. The tester steps on the snow with one foot and measures the penetration, with 30cm being considered enough to suggest a potential hazard. A refinement would attempt to factor in the weight and foot area of the tester. There are other such tests. Slope aspect is the compass direction the slope faces. Its hazard effect will be influenced by wind direction and exposure to the sun. The latter influence varies with the time of year. A good web site related to these issues with links to other sites is the Cyberspace Snow and Avalanche Center at http://www.csac.org/.

The bottom line is that a reasonably useful model will employ many variables, need extensive testing and refinement and require significant input from experienced avalanche personnel. It is clearly easier to apply the model in a developed ski area rather than in the back country. The computer models of which we are aware are mechanistic in nature, e.g., there is European work using finite element analysis. We feel that Fuzzy Logic is an appropriate tool and advance this article to explain the approach. We stress at the outset that this paper is expository and the model presented is not yet usable in a practical setting. However, we would approach a mature model by including new variables one at a time and testing the resulting software. Further, we have not even chosen the most important variables, but rather a handful that are easily understood.

Essential Elements of Fuzzy Logic

Articles and books describing Fuzzy Logic are widely available, as a cursory web search will quickly confirm. We recommend Earl Cox's book as a first, practical exposure (The Fuzzy Systems Handbook, AP Professional, 1994). First devised by Lotfi Zadeh (“Fuzzy Sets”, Information and Control, Volume 8, 338-353, 1965), Fuzzy Logic is best known for its applications in industrial control. However, it is also quite successfully used in decision-making applications, which is the basis of our project.

Fuzzy Logic is particularly appropriate in situations where a mathematical model is either unavailable or too unwieldy and where human expertise gleaned from experience and supported by intuition is available. In particular, it emulates the human reasoning process and employs linguistic forms in its modeling process. For this article the first author is the Fuzzy Logic programmer, and the second author provides the avalanche expertise.

In this article, we will introduce Fuzzy Logic via our problem space. This approach will give you insight into the concepts via a somewhat detailed example application. However, the scope of this article does not allow us to present Fuzzy Logic formally, nor in its full richness.

The minimal ingredients of a Fuzzy Logic model include these elements:

  • One or more input variables

  • A family of fuzzy sets for each input variable

  • One or more output variables

  • A family of fuzzy sets for each output variable

  • A group of rules connecting input and output variables

There are also algorithms which are applied to the model:

  • Fuzzification of crisp input variables

  • Application of the rules

  • Defuzzification of rule results to achieve crisp outputs

The terminology embedded in the preceding two lists will become familiar as we work through the Avalanche Predictor example.

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