Hazard evaluation of goaf based on DBO algorithm coupled with BP neural network

China is rich in mineral resources, and problems of goaf formed in the process of resource exploitation are serious obstacle to the development of China's economic, so it is of great significance for the assessment and management of goafs. This paper introduces emerging dung beetle optimizer (DBO) algorithm and establishes DBO-BP (back-propagation) model, at the same time, it is compared with a series of heuristic algorithms coupled with BP neural network models: PSO (particle swarm optimization) - BP model, WOA (whale optimization algorithm) - BP model, and SSA (sparrow search algorithm) - BP model. Then they are applied to evaluate the hazard of goafs, the result shows that the DBO-BP model gets the highest train set accuracy, which is at least 2.7 % higher than other models, while the DBO-BP model obtains the highest test set accuracy, meanwhile its effectiveness and stability have also been proven. Finally we apply the established DBO-BP model to evaluate the hazard of the tungsten mine goaf of Yaogangshan in Hunan Province, and its excellent practicability was confirmed. This paper may provide a reference for the solution of nonlinear engineering problems.