Landslide susceptibility evaluation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a landslide susceptibility prediction method and device, equipment and a storage medium. The method comprises the steps that at least one influence factor graph layer of landslide data of a to-be-predicted landslide area is acquired, wherein each influence factor graph layer comprises a grading result of influence factors corresponding to the to-be-predicted landslide area; and the at least one influence factor layer is input into a trained landslide susceptibility prediction model to obtain the probability of occurrence of landslide at each landslidesite in the to-be-predicted landslide area, wherein the landslide susceptibility prediction model is trained based on at least one historical landslide data. Therefore, the effect of quickly predicting the landslide susceptibility based on the plurality of influence factors in the whole landslide area is achieved.

Description

technical field [0001] The embodiments of the present invention relate to deep learning technology, and in particular to a landslide susceptibility evaluation method, device, equipment and storage medium. Background technique [0002] Landslide is one of the most common catastrophic natural disasters. There are many factors that cause landslides. How to use these factors to predict landslide susceptibility is the key to preventing and reducing landslide disasters. [0003] At present, using the neural network model to predict the landslide susceptibility is the main method. When using the neural network model to predict the landslide susceptibility, most of the data input into the neural network model are landslide-related data, which requires obtaining Landslide data, now landslide data are obtained from historical landslide reports and other documents, after obtaining the landslide data, extract the impact factors from the landslide data, organize the impact factors into s...

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Problems solved by technology

[0003] At present, using the neural network model to predict the landslide susceptibility is the main method. When using the neural network model to predict the landslide susceptibility, most of the data input into the neural network model are landslide-related data, which requires obtaining Landslide data, now landslide data are obtained from historical landslide reports and other documents, after obtaining the landslide data, extract the impact factors from the landslide data, organize the impact factors into structured data, and then input the structured data into the neural network In the model, the workload of data collation is relatively large, and the spatial information between regions is not considered; However, in this way, only the image of a single influencing factor at a single location can be extracted, and then the image can be input into the neural network model to predict the susceptibility of landslides

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