| Citation: | CHENG Gang，WU Yongfei，CAO Desheng，et al. Research and application of artificial intelligence algorithms in landslide monitoring and prediction technology[J]. Bulletin of Geological Science and Technology，2025，44（5）：1-15 doi: 10.19509/j.cnki.dzkq.tb20250115
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To reduce the risk of landslide disasters and further ensure regional sustainable development, conducting research effectively on landslide monitoring and prediction is of great practical significance. This paper aims to continuously improve the prevention and control level of landslide disasters by studying the key technologies and methods in landslide monitoring and prediction, and will analyze the efficiency and accuracy of various algorithms in scenarios of landslide monitoring and prediction.
In terms of feature extraction technology, the performance of three image feature matching algorithms, namely namely scale-invariant feature transform (SIFT), speeded up robust features (SURF) and assessment of scale-invariant features transform (ASIFT), were compared and analyzed. Comparative results demonstrate that ASIFT has significant advantages in the number of matches, precision rate and recall rate, and is especially suitable for complex environmental scenarios with high-accuracy requirements. In terms of optical flow analysis technology, the application effects based on the Lucas-Kanade sparse optical flow method and the Horn-Schunck dense optical flow method were discussed. Among them, the Lucas-Kanade sparse optical flow method has high computational efficiency and is suitable for real-time application scenarios, but there is a risk of missing important motion information. The Horn-Schunck dense optical flow method can provide comprehensive optical flow field information and is suitable for complex environmental scenarios. However, it has the drawback of high computational complexity and thus is difficult to be used in real-time processing. In terms of landslide susceptibility prediction, the advantages and disadvantages of the application of classic machine learning methods such as support vector machine (SVM), decision tree (DT), and random forest (RF) in landslide prediction are introduced in detail. The model performance based on particle swarm optimization-support vector machine (PSO-SVM) is mainly studied. This model optimizes hyperparameters, the classification accuracy, generalization ability and prediction accuracy of the model have been significantly improved. Furthermore, by introducing the Faster R-CNN model and leveraging its advanced convolutional neural network architecture, this paper realizes the automatic identification and classification of landslide events in complex scenarios, further enhancing the efficiency and accuracy of landslide monitoring and early warning.
The research shows that the accuracy rate of local feature extraction by ASIFT is 0.84, the tracking error of the Lucas-Kanade sparse optical flow method is 0.12, the root mean square error of the PSO-SVM model is 0.52, and the confidence level of the Faster R-CNN model in the automatic recognition and classification of landslide images can reach 0.98. The comprehensive performance is significantly improved compared with other algorithms in this paper.
In summary, by introducing artificial intelligence algorithms and integrating multi-disciplinary technical means, this paper has comprehensively enhanced the efficiency and accuracy of landslide monitoring and prediction technology. The research results provide more powerful technical support for the prevention and control of landslide geological disasters.
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