Intelligent Prediction of Water Quality Parameters Based on Improved Particle Swarm Optimization Coupled With Random Forest Model
Year: 2025
Type: article
Abstract: In recent years, the problem of water pollution has become increasingly serious, and traditional water quality monitoring methods are difficult to meet the demand for high precision. Constructing an efficient and reliable water quality prediction model is of great significance for governance decisions. To this end, this paper proposes a random forest (IPSO-RF) water quality prediction model optimized based on the improved particle swarm algorithm. Firstly, for the problems of traditional particle swarm algorithm (PSO), such as easy premature convergence and insufficient global search ability, an improved particle swarm algorithm (IPSO) with nonlinear iteration incorporating inertia weights is proposed, and its optimization performance is verified based on typical test functions. Secondly, the IPSO algorithm is combined with random forest (RF) to optimize the key hyperparameters of RF (e.g., the number of decision trees, the minimum number of samples for node splitting, etc.) in order to improve the prediction accuracy and generalization ability of the model. Simulation experiments were carried out based on the water quality monitoring data of a watershed, and the results showed that compared with RF and standard PSO-RF and other models, the IPSO-RF model showed lower MSE in the prediction of key indicators such as dissolved oxygen, phosphorus content of water body and ammonia nitrogen amount of water body, which verified its superiority in the prediction of water quality. This study not only provides new ideas for the application of intelligent optimization algorithms in the field of water environment, but also provides more accurate technical support for water quality monitoring and pollution prevention and control of environmental protection departments.
DOI: 10.61784/fer3030
Author: JinYi Luo
Institution: Chengdu University of Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411105284