| || Expert Fuzzy Model for Avalanche Prediction
Author : Vizhakat, Mohan
Source : Defence Science Journal ; Vol:53(4) ; 2003 ; pp 443-452
Subject : 53 Applied Physics;531 Mechanics;551.578.48 Avalanche
Keywords : Avalanche prediction;Fuzzification graph;Expert system;Expert fuzzy model;Algorithm;Avalanche prediction model;Fuzzy logic
Abstract : It is imperative that the time required for the analysis and of an extremely volatile event like avalanche needs to be reduced to the minimum. This is particularly critical because of the extremely fast and highly uncertain nature of the event itself. Another peculiar nature of such predictions is that these have to be based almost entirely on the long and intermediate-term data/information available, since there would hardly be any short-term warnings (unlike as in the case of a storm) that could point towards an imminent prediction. Both the above-mentioned factors favour adoption of such techniques of automated analysis, which are fast, accurate, and employable even under uncertain voids of information. Apart from empirical and statistical methods, one of the highly promising techniques for developing a practical model for prediction of avalanche is that based on rule-based expert systems. However, development of a realistic rule-based expert system based on conventional logic would imply that one has to firstly define the natural phenomenon being modelled at an extremely high resolution and accuracy. The process of defining a highly uncertain phenomenon like the avalanche at such high resolution, and thereafter, framing extensive rules for all the possibilities is likely to make the system extremely complex, and therefore, unmanageable in many ways. This study attempts to simplify this problem by proposing a simpler and better technique using an algorithm based on fuzzy logic. This algorithm has the potential to handle even highly complex phenomenon, like that of an avalanche in a fundamentally simple manner. Such potential makes it capable of handling the higher levels of details and still contains the complexity within the manageable limits. Additional details would also make the system more accurate and realistic.