Optimization of convolutional neural networks for predicting water pollutants using spectral data in the middle and lower reaches of the Yangtze River Basin, China

Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution. Deep learning (DL), as one of the most promising technologies today, plays a crucial role in the effective assessment of water body health, which is essential for water resource management. This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks (GAN). It integrates optimization algorithms (OA) with Convolutional Neural Networks (CNN) to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants. Specifically, after preprocessing the spectral dataset, data augmentation was conducted to obtain two datasets. Then, six new models were developed on these datasets using particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) combined with CNN to simulate and forecast the concentrations of three water pollutants: Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Finally, seven model evaluation methods, including uncertainty analysis, were used to evaluate the constructed models and select the optimal models for the three pollutants. The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations, while the GGACNN model excelled in TN concentration prediction. Compared to existing technologies, the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment, offering new insights and methods for water pollution prevention and control.