An enhanced deep learning model with data pre-processing for flow prediction in urban water supply networks

ABSTRACT One of the goals of efficient water supply management is to provide a regular supply of water pressure to meet the water needs of consumers. Water pressure control is closely related to consumer water demands, and an accurate prediction of water consumption is critical for effective water supply management. However, it is difficult to effectively predict the flow of water supply networks, which are characterized by uncertainty and instability. To obtain more accurate flow prediction data, this work proposes a new adaptive robust method for time series prediction modeling, called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory (CEEMDAN–KPCA–LSTM), in which CEEMDAN and KPCA preprocessing techniques are combined for determining flow prediction. First, the flow data are decomposed using the CEEMDAN algorithm to reduce non-smoothness. Then, KPCA is used to extract the key influencing factors from the feature series. Finally, the LSTM network is constructed to predict the water supply network flow using the results of the CEEMDAN and KPCA algorithms. The suggested scheme offers significant application prospects for water supply systems.