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- Title
改进的PSOGA-SVM模型应用于 露天矿区空气质量预测.
- Authors
李光明; 王军; 李颀
- Abstract
In order to grasp the air pollution caused by the open-pit mines, it is necessary to accurately predict the air quality of the open-pit mining area. PM10 concentration with the average temperature, relative humidity, light time, and wind were selected as the main factors affecting the air quality. Based on the collected data of the open-pit mining environment, a support vector machine(SVM) was used to establish a model. Particle swarm optimization(PSO) algorithm with improved inertia weight was introduced as mutation operator to optimize genetic algorithm, and finally the model was applied to the actual scenarios. An improved inertia weighted particle swarm optimization and genetic algorithm based optimize support vector machine(PSOGA-SVM) network prediction model was established based on MATLAB. The results show that the prediction accuracy of the proposed model is better than that of cross validation support vector machine(SV-SVM) and a particle swarm optimization for parameter optimization of support vector machine(PSO-SVM), and the prediction accuracy can reach more than 98.5%.
- Subjects
PARTICLE swarm optimization; SUPPORT vector machines; STRIP mining; GENETIC algorithms; AIR quality
- Publication
China Sciencepaper, 2019, Vol 14, Issue 12, p1348
- ISSN
2095-2783
- Publication type
Article