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The abrupt event monitoring is a challenging and critical issue in water environment systems. There are two main different abrupt events in the monitoring system, namely, the emergency water pollution accident and the abrupt sensor fault. The two different abrupt events have similar data characteristics, and few methods can be used to recognize the events. In this paper, a novel abrupt event monitoring approach based on kernel principal component analysis (KPCA) and support vector machines is proposed, which is combined with the physical redundancy method. The trust mechanism is introduced into the proposed approach to reduce the interference of external noise and improve the performance of quick response for the abrupt events. A spare data area is set up to store the data for the KPCA modeling. The data in the spare data area are updated continuously, and the KPCA model is updated subsequently to improve the adaptivity of the KPCA model for the abrupt event monitoring. The experimental results show that the proposed approach is capable of detecting and recognizing the two different abrupt events efficiently.