Characteristic extraction and control on severe, drought in Japan through timely and spacial pattern classification

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In this Grant, we proposed the objective function suite to pattern classification on hydrological and meteorological events. The following results are obtained ;i) ISODATA was introduced to classify the hydrological and metetrological data of spacial distribution of air pressure, air temperature and sea surface temperature. Similarity between feature pattern and sample one is formulated with fuzzy set, fractal and image processing theories.ii) Relation between above events and drought situation in Japan was analyzed through fitness rate.iii) Precipitation of drought season in Japan was estimated with fuzzy-neural network whose units were optimized by genetic algorithm (GA). Moreover GA classified Gauging stations of precipitation into several zones with same meteorological characteristics.iv) For real-time prediction of drought situation was taken with recurrent typed-neural network to operate reservoir against severe disaster. One reservoir was applied for the data of 1994 to verify the proposed methodologies.v) Drought damage due to global warming was estimated with the equivalent variation method by considering spatial and timely correlation with multi-point simulation method and.vi) New approach for occurrence probability of drought event was proposed by evaluating not only total volume but also feature pattern of precipitation with pattern recognition concept.