A water quality parameter prediction method based on transformer architecture and multi-sensor data fusion

Abstract

Water quality monitoring provides a basis for water quality control and water resources management. Prediction of water quality parameters can plan water use strategies, prevent further water pollution and improve water resource utilization efficiency. We propose a water quality parameter prediction method based on transformer architecture model and multi-sensor data fusion. The proposed multiple water quality parameter prediction model accepts multiple types of water quality parameter data input at the same time. The data embedding module integrates multiple types of water quality parameter information and assigns a unique position code to the data at each time step. The self-attention mechanism of the model mining the potential correlation between different time step data. The model can learn the internal relationship of the fusion data of multiple water quality parameters, and effectively predict the future trend of water quality parameters. The effectiveness of the proposed algorithm is verified by the measured data, and the advantages of the proposed method are verified by comparative experiments.