Jurg Schweizer and Paul M.B. Fohn
hazard supplemented with different supporting tools; input parameters and their relation. 2 Present approachesThe synoptical technique to assess the avalanche hazard for a given region still forms the basis of the decision making procedure of most avalanche forecast services. None of the supporting tools are, until now, reliable enough to substitute the human expert and will probably never be. But they may become an objective partner for "discussing"? A general overview of different methods is given in Fohn et al. (1977), Buser et al. (1985) and recently in McClung and Schaerer (1993) and Schweizer and Fohn (1994). In the following only some models and tools are mentioned.Operational systems based on the statistical approach using a long term data base were developed in several countries and are widely used (Buser et al., 1987; Navarre et al., 1987; Merindol,1992, McClung and Tweedy, 1994) both for local and for regional avalanche forecasting. The two most popular methods are the The aim of the purely A combined approach, containing deterministic and statistical components has been developed by Fohn and Haechler (1978). The total loading by snowfall, wind action and the settlement is simulated in order to forecast large, dry snow avalanches.
The French system MEPRA analyzes the snow cover stratigraphy; the snow profiles are simulated by the snow cover model CROCUS (Brun et al. 1989) running with meteorological data provided by SAFRAN (Durand et al., 1993), a model for optimal interpolation of meteorological data. Recently a hybrid expert system was developed using a neural network and rules extracted from the data base with neural network techniques (Schweizer et al., 1994).
In 1989 we started a new approach with the idea of building a system for regional avalanche forecasting comparable to the statistical ones but with optimized input and output parameters: called DAVOS. We tried to include some of the relevant physical processes, i.e. elaborated input parameters, and to give as result directly what the avalanche forecaster would like to have: the degree of hazard. (Schweizer et al., 1992). In 1991 we worked out a completely new approach, more physically based,comparable to a deterministic system, that tries to model the reasoning of the avalanche forecaster: called MODUL. Both systems are based on a software for inductive decision making: CYBERTEK-COGENSYS The CYBERTEK-COGENSYSTM The expert building up the system defines the input data needed to reach a particular decision, the output, and the criteria that are used to categorize or evaluate the data; each input parameter has to be grouped in logical ranges (up to five ranges). The expert "teaches" the Judgment Processor by entering examples and interpreting the situations represented by those examples. The Judgment Processor calculates the logical importance of each input parameter based on the observation of the mentor's decision. The logical importance is a measure of how a particular input parameter contributes to the logical model as a whole, based on how many situations within the knowledge base would become indistinguishable if that input parameter was removed. Based on the logical importance, given as a number from 1 ... 100, the parameters are classified as If a new situation is encountered the system tries to give a proposition for the possible decision on the basis of the past known situations. The similar situations are found by using the condition of similarity that prescribes that the majority of the values of the major input parameters has to be each in the same logical range. The quality of the proposed decision is described by the so-called confidence level, an indicator of how certain the system is that its interpretation is appropriate to the current situation: an exclamation mark (!) for very confident, a period (.) for reasonably confident or a question mark (?) for not confident. A low level of confidence suggests that there are few situations that the system considers to be logically similar, or that those situations that are similar have conflicting interpretations. Additionally the similar situations that are used to derive the decision with the according assertion level are also given. If the system is not able to find a decision on the basis of the present knowledge base it gives the result "not possible to make an interpretation-, in the following simply called "n.i." CYBERTEK COGENSYS The Judgment Processor's algorithm is not known in all details. Since the search for similar situations forms the core of the method, it may be called, in the broadest sense, a nearest neighbor method. However, the metric to search for similar situations differs substantially from the commonly used distance measure, e.g. the Euclidean distance. The categorization of the input data, the classification into major and minor parameters and the metric to-search similar situations are all non linear. The method is appropriate to deal with not independent, not normally distributed data. Briefly summarized the system weights and classifies the categorized data, searches for similar situations using strongly the classification and categorization, derives a result from the similar situations, describes the quality of the result and finally lists the similar situations used for deriving the result together with the pertinent similarity measure. The advantage of the method is the strong concentration on the input parameters that are considered as important. In our case the judgment problem is the The
MODUL. To each data set consisting of the above weather, snow and snow cover data belongs the description of the avalanche hazard, the output parameter. It seems most appropriate to choose as output of an expert system exactly the structure that is usually used by forecasters. So the assisting tool ~speaks" the same language as the forecaster.The avalanche hazard is formulated first of all as degree of hazard ( 1... 7). Secondly, the lower limit of the primarily endangered altitudes is given in steps of usually 200m The avalanche hazard, as we use it, is the result of an "a posteriori" critical assessment of the hazard, the so-called Operationally the verification has been done some days later considering the observed avalanche activity (naturally and artificially released), the past weather conditions, the additional snow cover tests, the backcountry skiing activity and several other, partly personal observations. Snow cover tests form an important part of the verification work. The verification is an expert task itself and describes the avalanche situation for a given day probably still not yet exactly, but more accurately than the public avalanche forecast. Whereas the avalanche forecast is correct in about 70% of the days, the verification may be correct in about 90% of the days. By the way, the weather forecast achieves 80 to 85% of correct diagnosis.
parameters (see below).Beside the input parameters we also have chosen the ranges for each of the input parameters according to our experience (Table 2). Based on the 9 year database we are finally able to check whether the chosen ranges were reasonable or not. One example, the 3-day-sum of new snow depth, is given in Figure 2. The situations with sum of new snow between 30 to 60cm and 60 to 1 20cm seem to be quite similar. In most situations the degree of hazard is 3 for both ranges. Hence it seems that these ranges do not categorize well. However, it is clear that the sum of new snow depth is only one of the input parameters that are all interconnected somehow, and that the avalanche hazard can not be determined by a sole input parameter. The The The original version of the model DAVOS was called DAVOS1. The experience with this version has given rise to develop further
new snow depth with the degree of hazard forall situations (1361) in the last 9 winters to check whether the logical ranges chosen categorize the data appropriately. DAVOS31 and DAVOS32 were born from the idea that it is generally important whether for a given day there is new snow or not. Accordingly the knowledge base was split into situations without new snow and ones with new snow. Finally we tested a version (DAVOS4) that only gives the degree of hazard, and not also the altitude and the aspect of the most dangerous slopes. Due to the single type of output the version DAVOS4 should discriminate better than the other versions and hence should give better results.
First of all it is decisive whether there is new snow or not. Either the forecaster has to assess the new snow stability or he directly assesses - without new snow - the old snow stability which is often similar to the stability one day before, except if there is e.g. a large increase of heat transport and/or radiation. So he structures the input data according to the different steps in the decision process. If both the new snow stability and the old snow stability, including both the effect of the weather as forecast for today, are decided, the two release probabilities are combined. Taking into account the effect of the terrain and of the skier as trigger the degree of hazard is finally determined. At the moment only the degree of hazard is given; the altitude and the aspect as given in the DAVOS model is not yet implemented.
and their relation. Each of the
; principally dependent on the Combined{natural) release probability and the Influence of the skier, but also Overall critical depth dependent on the by the potential avalanche size Depth of stable old snow and volume and on the by the terrain roughness. . Input parameters used in the model MODUL.Table 4The output result of a subproblem is usually used as input parameter in another subproblem that appears later on in the decision process. Many of the input parameter values are calculated using rules that depend them selves on the input values. The Due to the modular structure it is easily possible to make So the important subproblem In operational use, the model has to be run interactively by an experienced user. The model stops if the proposed decision in one of the subproblems does not have a high confidence level, and the user has to confirm the decision before the model continues to run. The final output result, the degree of hazard, is well explained by the output results of the different subproblems. If the model proposes a different degree of hazard than the user has independently estimated, the difference becomes usually obvious by inspecting the output results of the subproblems. Due to this feature the model is not at all a black box system, but a real supporting tool for the forecaster. The interactive use of the model proved to be very instructive.
To rate the interpretations provided by the system we defined the requirements of quality given in Table 5. Four steps of quality for the given interpretations are defined: DAVOS model. Considering the degree of hazard, the altitude and the aspect, the versions DAVOS1 and DAVOS2 have on the average a performance of about 65% and 70% To be able to compare the results of the versions DAVOS1 and DAVOS2 to the results of different systems, it is more convenient to only consider the degree of hazard. In that case in 52% and 54% of all situations the degree of hazard was correct compared to the verification for DAVOS1 and DAVOS2 respectively. 86% and 89% of all situations respectively are correct or deviate +/- 1 degree of hazard from the verification. The versions DAVOS31 and DAVOS32, being complementary to one another, represent a certain improvement; the combined average performance is 61%. The version DAVOS4 that only predicts the degree of hazard is on the average correct in 63% of all situations. This result represents the best performance of the different versions of model DAVOS. However, considering the performance degree by degree the result is rather disillusioning. The performance for the intermediate degrees 2 and 3 is only 55% and 57%, respectively. These degrees are of course most difficult to forecast. In the case of low or very high hazard the data is more often unambiguous. The extremes are easier to decide. However, since the extreme events at the upper margin of the scale are rare, the correctness is also not too good for these degrees of hazard (59%). The performance results show quite clearly that probably all statistically based models based on real situations are notable to predict exceptional situations correctly, since this sort of situations are rare.
MODUL compared to the verified degree of hazard for the winter 1993/94 in the Davos area. The experience shows that the more deterministic model MODUL is much more sensitive to single input parameters. A wrong input parameter or a wrong decision in one of the subproblems may have substantial consequences at the end, i.e. a change in the degree of hazard of 1 or 2 steps. So the reaction on a small change may sometimes be drastic. This is especially due to the smaller number of input parameters treated at once in a subproblem, also due to the fact that the output result of a subproblem often is used again as input in another subproblem, and partially due to the fact that the input data is strictly categorized. The latter problem might be removed by introducing fuzzy logic, i.e. defining blurred categories. Figure 5 is a comparison of the correctness compared to the verified degree of hazard for the different forecasting models DAVOS1, DAVOS2, DAVOS4 and MODUL for the Davos area during the last three winters (1991/92 to 1993/94). It is clear that the more input parameters or the less complex the result, the better the performance.
the four different forecast models DAVOS1, DAVOS2, DAVOS4 and MODUL during the three winters 1991/92 to 1993/94. The deviation from the verified degree of hazard in the Davos area is given
The CYBERTEK-COGENSYS The snow cover data proved to be very important. Actually it is well known that avalanche forecasting depends strona~ly on the state of the snow cover. However, except the French model MEPRA, until now none of the present models took into account this obvious fact. Of course this sort of data is not easily available but it is an illusion to expect that a supporting tool without any snow cover data is as powerful as the expert forecaster. Meteorology plays an important role, but not the decisive one. The interactive use of the models proved to be a substantial advantage as especially the model MODUL is very instructive. It is well appropriate for the training of junior forecasters with a certain basic knowledge. The model DAVOS - comparable to a statistical model - and the model MODUL - more comparable to a deterministic type of model - achieved a performance of about 60% and 70 to 75%, respectively. There exist no comparable or similar results. based on a long term operational test of any different system for regional avalanche forecasting.
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