Concealed Geohazard Risks and the Operation of Artificial Intelligence Technology
DOI:
https://doi.org/10.54097/vraejz62Keywords:
Geohazard Monitoring, Optical Fiber Sensing, Radar Remote Sensing, Artificial Intelligence, Multi-Source Data Fusion, Intelligent Early WarningAbstract
Geohazard monitoring technology constitutes a critical approach for disaster risk prevention and control. This paper systematically reviews innovative applications and challenges of optical fiber sensing, radar remote sensing, artificial intelligence (AI), and related technologies in geohazard monitoring. By analyzing the mechanisms and case studies of distributed optical fiber sensors (DOFS), SBAS-InSAR, UAV remote sensing, and deep learning models, this study reveals the pivotal role of multi-source data fusion and intelligent algorithms in early warning systems for landslides, rockfalls, and other geological hazards. The research identifies existing bottlenecks, including insufficient adaptability to complex environments and limited model interpretability. Future breakthroughs are proposed through interdisciplinary technology integration, adaptive algorithm optimization, and sensor material innovation. This paper provides theoretical foundations and technical references for advancing the intelligence and integration of geohazard monitoring technologies.
References
[1]Abdalzaher, M. S., Krichen, M., & Falcone, F. (2023). Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions. Earth-Science Reviews, 240, 104367.
[2]Bovenga, F., et al. (2022). Long-term InSAR analysis for slope instability. Remote Sensing, 14(7), 1677.
[3]Buchoud, E., et al. (2016). Quantification of Submillimeter Displacements by Distributed Optical Fiber Sensors. IEEE Transactions on Instrumentation and Measurement, 65(2), 413–422.
[4]Chen, Y., Tao, Q., Hou, A., Ding, L., Liu, G., & Wang, K. (2020). Accuracy verification of Sentinel-1A in monitoring mining subsidence. Journal of Applied Remote Sensing, 14(1), 014501.
[5]Cigna, F., & Tapete, D. (2021). Satellite InSAR for mapping and monitoring of geological hazards. Remote Sensing, 13(7), 1228.
[6]Dai, K., et al. (2020). Early identification of potential landslides using InSAR. Engineering Geology, 276, 105757.
[7]Gariano, S. L., & Guzzetti, F. (2016). Landslides in a changing climate. Earth-Science Reviews, 162, 227–252.
[8]Ghazali, M. F., & Mohamad, H. (2019). Monitoring subsurface ground movement using fibre optic inclinometer sensor. IOP Conference Series: Materials Science and Engineering, 527(1), 012040.
[9]Gong, H., Pan, Y., Zheng, L., Li, X., Zhu, L., Zhang, C., ... & Zhou, C. (2018). Long-term groundwater storage changes and land subsidence in the North China Plain. Hydrogeology Journal, 26(5), 1417–1427.
[10]Han, H., Shi, B., Zhang, C.-C., Sang, H., Huang, X., & Wei, G. (2023). Application of ultra-weak FBG technology in real-time monitoring of landslide shear displacement. Acta Geotechnica, 18(5), 2585–2601.
[11]He, H., et al. (2020). Distributed temperature sensing for soil monitoring. Advances in Agronomy, 148, 173–230.
[12]Huang, F., Zhang, J., Zhou, C., Wang, Y., Huang, J., & Zhu, L. (2020). A deep learning algorithm for landslide susceptibility prediction. Landslides, 17, 217–229.
[13]Intrieri, E., et al. (2015). Sinkhole monitoring and early warning using GB-InSAR. Geomorphology, 241, 304–314.
[14]Jibson, R. W., & Harp, E. L. (2000). Method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58(3), 102–106.
[15]Li, C., Tang, J., Cheng, C., Cai, L., & Yang, M. (2021). FBG arrays for quasi-distributed sensing: A review. Photonic Sensors, 11(1), 91–108.
[16]Li, Y., et al. (2019). Saito’s three-stage creep theory for landslide prediction. Landslides, 16(5), 921–932.
[17]Li, Z. (2021). Recent advances in earthquake monitoring I: Revolution of seismic instrumentation. Earthquake Science, 34(2), 177–188.
[18]Lian, C., et al. (2014). Landslide displacement prediction using machine learning. Engineering Geology, 182, 90–101.
[19]Liu, X., Wang, Y., Zhang, H., & Guo, X. (2023). Susceptibility of marine geological disasters: An overview. Geoenvironmental Disasters, 10(1), 10.
[20]Ma, Z., & Mei, G. (2021). Deep learning for geological hazards analysis: Data, models, applications, and opportunities. Earth-Science Reviews, 215, 103551.
[21]Marra, G., et al. (2018). Ultrastable laser interferometry for earthquake detection with submarine cables. Science, 361(6401), 486–490.
[22]Osmanoğlu, B., et al. (2016). Time series analysis of InSAR data for landslide monitoring. Remote Sensing, 8(3), 237.
[23]Qin, L., & Kang, L. (2016). Technical framework design of safety production platform based on IoT. Industrial Mine Automation, 42(1), 5–9.
[24]Schenato, L., et al. (2017). A review of distributed fibre optic sensors for geo-hydrological applications. Applied Sciences, 7(9), 896.
[25]Tang, H., et al. (2022). 3D landslide evolution modeling using SAR and deep learning. Remote Sensing of Environment, 276, 113043.
[26]Xu, Q., Dong, X., & Li, W. (2019). Integrated space-air-ground investigation system for geohazards. Geomatics and Information Science of Wuhan University, 44(7), 957–966.
[27]Xu, Q., et al. (2018). Study on successive landslide damming events in Baige village. Journal of Engineering Geology, 26(6), 1534–1551.
[28]Xue, Y., et al. (2021). Challenges and countermeasures for Sichuan-Tibet Railway. Innovation, 2(2), 100105.
[29]Yang, A., et al. (2022). Two megafloods in Yarlung Tsangpo River since last-glacial period. Global and Planetary Change, 214, 102876.
[30]Zhao, T., Wang, S., Ouyang, C., et al. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. The Innovation, 5(5), 100691.
Zheng, Y., Huang, D., & Shi, L. (2018). A new deflection solution for FBG-based inclinometer in slope monitoring. Measurement Science and Technology, 29(5), 055008
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Qiang Liu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
