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

在本研究中，我们提出了水文气象事件模式分类的目标函数集。取得了以下成果：i)引入ISODATA对大气压力、气温和海面温度的空间分布的水文和计量数据进行分类。利用模糊集理论、分形理论和图像处理理论建立了特征模式与样本模式的相似度。ii)通过适应度分析上述事件与日本旱情的关系。iii)采用模糊神经网络估计日本旱季降水量，并用遗传算法对模糊神经网络的单元进行优化。4)采用递归型神经网络进行实时旱情预报，实现水库抗灾调度。以某水库1994年的资料为例，对所提出的方法进行了验证。(5)利用多点模拟方法考虑空间和时间相关性，用等效变差法估算了全球变暖造成的旱灾损失；(6)提出了一种新的旱灾发生概率计算方法，该方法不仅考虑了降水总量，而且考虑了降水的特征模式。

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.