Advanced deep learning model for predicting water pollutants using spectral data and augmentation techniques: A case study of the Middle and Lower Yangtze River, China

Abstract

Deep learning has demonstrated significant advantages in managing nonlinear relationships within high-dimensional spectral data, making it widely applicable in water quality monitoring. However, the variety of model selection and construction strategies has resulted in substantial fluctuations in predictive performance, particularly with high-dimensional data. This study constructs an integrated deep learning framework for predicting water pollutant concentrations, incorporating several key modules including data preprocessing, frequency decomposition, feature enhancement, sample augmentation, and decoder regression prediction. In the established model, an improved wavelet transform algorithm is first employed to address the issue of original data being unable to effectively distinguish detailed features, thereby accurately extracting the periodicity and volatility characteristics of the data. Secondly, an encoder module based on the Informer architecture enhances various frequency domain features and further improves the quality of features and their correlation with labels through distillation techniques. Subsequently, an improved generative adversarial network is introduced to tackle the problem of small sample data by effectively augmenting the limited dataset, thereby enhancing the overall quality of the dataset. Finally, a decoder module combining an optimization algorithm and an improved convolutional neural network (IMCPSO-RCNN) effectively addresses the shortcomings of traditional models in hyperparameter optimization and predictive performance, achieving efficient and accurate regression prediction of pollutant concentrations. A case study in the middle and lower reaches of the Yangtze River shows that this model outperforms others in prediction accuracy, achieving coefficients of determination (R²) of 0.9785, 0.9733, and 0.9741 for TN, COD, and TP, respectively. The root mean square error (RMSE) values are 0.0601, 0.6248, and 0.0023, while the mean absolute error (MAE) scores are 0.0252, 0.2810, and 0.0006, respectively. The necessity and effectiveness of each model component are validated through ablation experiments. This research offers an efficient and unified deep learning solution for monitoring water pollutants.

Synopsis

This deep learning framework enhances water quality monitoring by accurately predicting pollutant concentrations, informing environmental policy and water system management.