Abstract
To discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super‐Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine‐tuned and applied to highway landslide monitoring and surveying. Finally, an entropy weight gray clustering evaluation method based on data mining analysis is proposed, and the performances of several methods are verified. The results show that the average score of the BRISQUE algorithm in Image Quality Assessment (IQA) is above 0.9, and the average running time is 0.1523 s. The combination of BRISQUE and SRCNN can improve the image quality significantly. After fine‐tuning, the recognition accuracy of AlexNet for landslide images can reach about 80%. The evaluation method based on gray clustering can effectively determine the correlation between soil moisture content and slope angle and thereby be applied to the analysis and evaluation of highway landslides. The results are beneficial to the judgment and assessment of highway landslide conditions, which can be extended to research on other geologic hazards.
Highlights
With the prompt development of advanced technologies, the quality of human life has been improved significantly
Su et al (2017) tested the influence of coal mining on landslides in coalmining areas by comparing the landslide sensitivity maps drawn by three nonlinear methods; the results revealed that the Support Vector Machine (SVM) model had better
The brightness value of the image formed on the surface of the object is affected by the microstructure composition of the object surface, the change in the incident light distribution, and the relative orientation between the object and the light source. is induces the difference in the radiation intensity received by the machine vision; in addition to the influence of environmental factors, the final image quality will be damaged. is is especially common in geologic hazards such as highway landslides; the defogging of the image is more important
Summary
With the prompt development of advanced technologies, the quality of human life has been improved significantly. Darya et al (2017) employed remote sensing technology to quantitatively assess regional-scale landslide disasters under the premise of insufficient data; through an automatic identification method, the susceptibility and damage of landslides with 30-year time series obtained by satellite remote sensing data were evaluated; the results suggested that satellite remote sensing had huge potential in acquiring temporal and spatial information about landslides, which could improve landslide hazards effectively, especially in areas with insufficient data such as Kyrgyzstan [5]. There is less research that combines big data technology and deep learning on landslides On this basis, to explore the evaluation and analysis of highway landslides, the Image Quality Assessment (IQA) algorithm of deep learning without image reference set is introduced. The gray clustering assessment method based on data mining analysis is introduced to study the monitoring and survey of highway landslides. e results are expected to provide an effective method for the prevention and control of highway landslide geologic hazards
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