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It is very difficult for operators to predict the progression of a loss of coolant accident (LOCA) because nuclear plant operators are provided with only partial information during the accident or they may have insufficient time to analyze the data despite being provided with considerable information. Therefore, its break location should be identified and the break size should be predicted accurately in order to provide the operators and technical support personnel with important and valuable information needed to successfully manage the accident. In this paper, support vector machines (SVMs) are used to identify the break location of a LOCA and predict the break size using the support vector classification (SVC) and support vector regression (SVR), which are well-known application areas of SVMs. The SVR models to predict the break size were optimized using a genetic algorithm. The inputs to the SVMs are the time-integrated values obtained by integrating the measurement signals in a short time interval after a reactor scram. The results showed that the proposed algorithm identified the break locations of LOCAs without fault and predicted the break size accurately.