Application of artificial neural network model based on GIS in geological hazard zoning

Under specific terrain and climatic conditions, it is extremely easy to cause various types of geological hazards, and the occurrence of geological hazards will affect people's production and life, with the consequence that the economic losses are extremely great, and even severely endanger human life. However, the existing geological hazard danger zoning is slightly insufficient in accuracy and operating efficiency, and the effect in practical application needs to be further improved. In view of the above problems, this paper proposes a study on the application of GIS-based artificial neural network models in the geological disaster risk zoning. This article first expounds the related concepts of geological hazards zoning and gives the principles to be followed. Then, the calculation of evaluation factors and risk level probabilities are proposed, and a hierarchical model is constructed using the analytic hierarchy process. The weight of each evaluation factor is multiplied with the information to obtain the weighted information. Calculate the probability based on the topographic features, stratigraphic lithology, and terrain slope. Combine the artificial neural network with BP neural network to predict the results. Through the simulation experiments on the geological data of the low-mountain and hilly areas in Wanli District, Nanchang, the results show that the proportion of high-probability-prone areas and medium-probability-prone areas predicted by this paper is as high as 91.87%, and the evaluation results are consistent with the actual disaster occurrence better. The area under the ROC curve was 87.3%, which also verified the effectiveness of the method in this paper.

Automated summary

This study proposes a research on the application of GIS-based artificial neural network models in geological disaster risk zoning, constructing a hierarchical model using the analytic hierarchy process and combining artificial neural networks with BP neural networks for prediction. Through simulation experiments in the low-mountain and hilly areas in Wanli District, Nanchang, the method predicted a high proportion of high and medium-risk areas with results consistent with actual disaster occurrences.