Water Level Prediction Based on Improved Spectral Residual Preprocessing and Convolution Neural Network

Time series prediction is one of the important tasks of data mining, which provides key guidance for a lot of work, such as financial stock prediction, water level prediction, and so on. The preliminary work of time series prediction includes data anomaly detection, missing time series repair, and other tasks. Aiming at all kinds of water level and discharge time series data around Chaohu lake, this paper proposes a convolution neural network prediction model combined with a significance detection algorithm. The preprocessing module of the model makes use of the improved significance detection algorithm of computer vision and a principle of electric field repair. Taking the water level and discharge of each station around Chaohu lake from June to August 2020 as convolution neural network (CNN)input and the water level of Zhongmiao station in Chaohu lake in the next 5 days as prediction output, an excellent prediction result with MAE of 0.056m is obtained. At the same time, it is proved that the computer vision algorithm is feasible in dealing with one-dimensional time series.