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

  • S.I. : DPTA Conference 2019
  • Published:
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Abstract

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.

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Acknowledgements

This work was supported by National Key R&D Plan of China (2018YFC1505505). This work was supported by SKLGP2015K016. (General Foundation Project at State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection: Study on the mechanism of rock mass damage and instability during freezing and thawing in cold alpine mountainous regions); Ministry of Transport Technology Demonstration Project (2016009), Ministry of Transport Technology Demonstration Project (2016010), Sino-Ukrainian Science and Technology Exchange Project (CU03-32).

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Authors and Affiliations

  1. School of Civil Engineering, Beijng Jiaotong University, Beijing, 100044, China

    Qulin Tan & Jiping Hu

  2. Institute of Spatial Information for Line Engineering, Beijng Jiaotong University, Beijing, 100044, China

    Qulin Tan & Jiping Hu

  3. Xinjiang Communication Construction Co. Ltd. (XCCG), Urumqi, 830000, Xinjiang, China

    Yong Huang

  4. Chengdu University of Technology, Chengdu, 610000, Sichuan, China

    Yong Huang

  5. Transpotation Industry Highway Maintenance Collaborative Innovation Platform Under Complicated Conditions of Western China, Urumqi, 830000, Xinjiang, China

    Yong Huang

  6. Western Sub-Alliance of Zhongguancun Zhongke Highway Maintenance Technology Innovation Alliance, Urumqi, 830000, Xinjiang, China

    Yong Huang

  7. School of Computer and Information Technology, Beijng Jiaotong University, Beijing, 100044, China

    Jun Hu

  8. China Institute of Geo-environment Monitoring, Ministry of Natural Resources, Beijing, 100081, China

    Pinggen Zhou

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  1. Qulin Tan
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Correspondence to Yong Huang.

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Tan, Q., Huang, Y., Hu, J. et al. Application of artificial neural network model based on GIS in geological hazard zoning. Neural Comput & Applic 33, 591–602 (2021). https://doi.org/10.1007/s00521-020-04987-4

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  • DOI: https://doi.org/10.1007/s00521-020-04987-4

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