Abstract
The current model design for environmental governance cost prediction is too simple, it is difficult to obtain the ideal prediction accuracy, and it has the disadvantages of slow convergence. Based on this, this study combines the particle swarm optimization algorithm to improve the support vector machine and proposes a machine learning method based on particle swarm optimization support vector machine. Through the analysis of the machine learning process and the actual project of environmental governance, this study constructs a scientific predictive index system, proposes a predictive model based on particle swarm optimization parameters, and uses system clustering analysis to classify similar sample data. At the same time, this study compares the performance of BP neural network model-based prediction model, LSSVM model-based prediction model, and PSO-LSSVM model-based prediction model. The research indicates that the prediction model based on PSO optimization LSSVM has a good guiding significance for the cost prediction of environmental governance engineering, and is more suitable for the prediction of the pre-cost of environmental governance.
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This research is supported by the National Natural Science Foundation of China (No. 71773032).
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Ai, D., Yang, J. A machine learning approach for cost prediction analysis in environmental governance engineering. Neural Comput & Applic 31, 8195–8203 (2019). https://doi.org/10.1007/s00521-018-3860-z
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DOI: https://doi.org/10.1007/s00521-018-3860-z