Early warning modeling and application based on analytic hierarchy process integrated extreme learning machine

In order to deal with complex food inspection data and accurately predict food safety risks, this paper proposes a predictive modeling approach based on analytic hierarchy process (AHP) integrated extreme learning machine (ELM) (AHP-ELM). The proposed approach utilizes the AHP model to obtain the effective risks characteristic information (RCIs). Compared with the analytic hierarchy process (AHP) integrated traditional artificial neural network (ANN) approach, the robustness and effectiveness of AHP-ELM model were validated through the dairy product inspection data source from the Analysis and Testing Institute of one province in China. Finally, the RCIs and the prediction value are obtained to provide more reliable food information and identify potential emerging food safety issues. The proposed method is applied to the food safety early warning and monitoring system in China. The result shows that the proposed model meets the needs of the national food safety policy for early warning. The model would serve as an operation guide for the food safety risks discovery and early warning.