In the context of the development of artificial intelligence, machine learning is one of the most important techniques in the field of artificial intelligence. At the same time, more and more practitioners have begun to try to utilize predictive models in machine learning to solve problems in practice. However, as water pollution has become a global environmental problem, traditional water quality prediction methods are difficult to cope with the complex and dynamically changing water environment. This project is dedicated to combining Particle Swarm optimization (PSO) and Back Propagation Neural Network (BPNN) by optimizing the weights and thresholds of the BP neural network, as well as dynamically tuning the parameters to optimize the PSO, and outputting a coupled PSO-BP model, thus overcoming the limitations of traditional water quality assessment methods. It effectively improves the global search ability of the model and avoids falling into local optimization. This method can also be widely used in the fields of environmental monitoring, financial forecasting, medical diagnosis, etc., demonstrating the prospect of wide application of artificial intelligence in solving practical problems.

Yiwei Huang, Xiang Wei. Improved BP neural network prediction model based on particle swarm algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 143-151. https://doi.org/10.25236/AJCIS.2024.070519.

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