Data and knowledge-driven Grey Factorization Machine prediction model for safety risk in water conservancy engineering construction
[Objective]The existing data-driven safety risk prediction method for water conservancy engineering construction is insufficient in the mining and utilization of domain knowledge,and the accuracy and interpretability of the prediction result need to be further improved.In order to establish a data and knowledge-driven safety risk prediction model for water conservancy engineering construction,[Methods]a domain knowledge enhanced Grey Factorization Machine is proposed by combining Grey Clustering and Factorization Machine.Firstly,Grey Clustering based on Possibility Function is introduced to represent the prior knowledge of safety risks from the experts in the field of water conservancy engineering construction.Then,prior knowledge is incorporated into Factorization Machine model in the form of parameters to construct a data and knowledge-driven Grey Factoriza-tion Machine.Finally,a method for calculating model parameters is provided based on Random Gradient Descent,and the model is applied to a case to verify its effectiveness.[Results]The application result show that compared with traditional Factorization Machine,Grey Factorization Machine's Accuracy,Precision,Recall and F1 Score are improved to varying degrees.Compared with Support Vector Machines,Deep Factorization Machine and other benchmark models,Grey Factorization Machine also has better predictive performance.[Conclusion]It indicates that the data and knowledge-driven Grey Factorization Machine can more accurately predict safety risks,and provide better decision-making support for safety risk management in water conservancy engi-neering construction.
国家科技期刊平台
NSTL
万方数据
维普
