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Analysis of radar technology identification model for potential geologic hazard based on convolutional neural network and Harris Hawks optimization algorithm

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Abstract

To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built based on identification of CNN image and calculation of big data algorithm, which can effectively improve the geologic hazard identification accuracy. Through experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent identification under CNN and big data is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase in training samples, the identification accuracy of various network models is also improved. Through radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is proposed for data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. To sum up, this research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

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Acknowledgements

1. This work was supported by Supported by SiChuan key research and development plan “geological hazard identification technologies of mountain tourist line based on big data”(No. 2020YFS0354). 2. This work was supported by Supported by Scientific Research Fund Project of Yunnan Provincial Department of Education “Earthquake relief asymmetric information game dynamics model in complicated landforms” (No. 2020J0381).

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Authors and Affiliations

  1. School of Urban and Environmental Sciences, Yunnan University of Finance and Economics, Kunming, 650221, People’s Republic of China

    Feng He, Chunxue Liu & Guangjing Bao

  2. School of Tourism, Leshan Normal University, Leshan, 614000, People’s Republic of China

    Hongjiang Liu

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  1. Feng He
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  3. Chunxue Liu
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Communicated by Mu-Yen Chen.

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He, F., Liu, H., Liu, C. et al. Analysis of radar technology identification model for potential geologic hazard based on convolutional neural network and Harris Hawks optimization algorithm. Soft Comput 27, 3493–3507 (2023). https://doi.org/10.1007/s00500-021-06206-1

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