Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction.

Water quality prediction is the basis for the prevention and control of water pollution. In this paper, to address the problem of low prediction accuracy of existing empirical models due to the non-smoothness and nonlinearity of water quality series, a novel water quality forecasting model integrating synchrosqueezed wavelet transform and deep extreme learning machine optimized with the sparrow search algorithm (SWT-SSA-DELM) was proposed. First, the water quality series was denoised by SWT to reduce the non-stationarity and randomness of water quality series.

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation.