Research on a Full Spectrum COD Long Short-Term Memory Network Prediction Model Based on Residual Connection

Chemical oxygen demand (COD) is an important parameter for measuring the degree of water pollution, and its accurate prediction is of great significance for water quality monitoring. This study aims to improve the prediction accuracy of seawater COD by fully exploring the temporal and complex characteristics of water quality spectral data. The study applied preprocessing and wavelength selection methods to process raw spectral data, and constructed a full spectrum COD long short-term memory network prediction model based on residual connection. This model effectively solves the problem of gradient vanishing in deep neural networks by integrating residual connection. At the same time, a bidirectional long short-term memory network is introduced to capture temporal features in spectral data, thereby enhancing the model's ability to learn potential patterns and complex dependencies. The experimental results show that compared with other basic models, this method significantly improves prediction accuracy and exhibits good robustness in error control, providing a more effective means for accurate prediction of water quality COD.