Abstract
In this paper an attempt has been made to use artificial intelligence in avalanche forecasting and to develop arule-based expert system for predicting direct action avalanches of Chowkibal-Tangdhar axis (JLK). Using C-language integrated production system (CLIPS), procedural knowledge is represented in the form of rules. Thecondition attributes of the rulebased system are 28 variables selected from 1 154 samples of snow-met and snowprofile data.The relative contribution of each variable on avalanche days and non-avalanche days and their influence on sitewise release of avalanche was studied to formulate 358 rules. These rules, which include 173decision rules, were finally implemented and validated for running the model. Sixty-three samples of snow-metdata and pit profile data attributing to avalanche days and 54 samples of non-avalanche days were run on themodel. The results show that the knowledge-based model can predict avalanche days with 76 per cent efficiency.The misclassified results accounted for 28.2 percent of 117 test samples.
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