Improved Particle Swarm Optimization Algorithm Combined with BP Neural Network Model for Prediction of Total Phosphorus Concentration ! in Water Body Using Transmittance Spectral Data

The accurate detection of pollution levels in water bodies using transmission spectrum data and fusion algorithms has become crucial for safeguarding water resources, Inaccurate predictions and detection frequently result from the high dimension of transmission spectrum data and model instability. The Yangtze River water body's total phosphorus concentration content is predicted in this study, and an accurate and environmentally friendly approach is suggested to achieve this goal. In particular. maxi min normalization and mean centering are two preprocessing operations carried out on the Yangtze River's measured water quality transmission spectrum data. These operations remove noise while eradicating differences between different data magnitudes, guaranteeing the consistency and reliability of the data. In addition, to solve the problem of the high dimension the transmission spectrum data, the KPCA method is used to reduce the dimension of the data and extract the features. The KPCA method is used to select the top 6 principal components that represent 99.42% of the information content of the original data for subsequent prediction model training by finding a classification plane in a high dimension space, Then, the foundation of the initial particle swarm algorithm, the particle initialization rule, multiple swarm competition strategy, parameter adaptive update strategy, population diversity guidance strategy, and particle variation mechanism are added to improve the particle warm's capacity for optimization and prevent particles from trapping in the local optimal solution. Additionally, the improved article swarm algorithm optimizes the initialized weights and parameter values in the BP neural network to accelerate the convergence of the network and improve prediction performances, Finally, the total phosphorus content of the samples in the test set was predicted using the IMCPSO-BPNN model. The experimental results showed an R-2 of 0.975 786, an RMSE of 0.002 242, and an MAE of 0.001 612. The IMCPSO-BPNN model suggested in this work has a better fitting effect and better Accuracy in forecasting the total nitrogen concentration in the Yangtze River water body when compared to other models such as The RF model, the BPNN model, and the PSO-BPNN model. It offers fresh concepts and viewpoints for studying and applying predictive modeling using transmission spectrum data and fusion algorithms to protect water resources and environmental management.