Fast and accurate detection of nature or artificial water contamination events plays a vital role in protecting the water environment and public health.To solve the problem that conventional water quality anomaly detection algorithms have bad performance under high background noise

an algorithm based on RBF neural network and wavelet analysis is proposed.By introducing RBF neural network to predict future water quality values using previous observations of the water quality

wavelet de-noising residual time series is obtained by comparing prediction value with real value

then using wavelet de-noising in sliding windows

if the new residual at one time is greater than specific threshold/baseline

water quality is claimed abnormal.Using the online monitoring ammonia nitrogen value as the research object

it proves that the proposed algorithm has lower false alarm rate（FAR）and higher probability of detection（PD）comparing with time series increments.