Spatial prediction of highway slope disasters based on convolution neural networks

• Published in: Natural hazards (Dordrecht) 2022-09, Vol.113 (2), p.813-831
• Main Authors: Yin, Chao, Wang, Zhanghua, Zhao, Xingkui
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Civil Engineering; Convolution; Disasters; Drills; Earth and Environmental Science; Earth Sciences; Elevation; Environmental Management; Geographical information systems; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Horses; Hydrogeology; Natural Hazards; Neural networks; Original Paper; Prediction models; Slopes; Success
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-022-05325-8

In order to clarify the spatial differentiations of highway slope disasters (HSDs) in Boshan District, spatial prediction was carried out based on ECG-CNN with the support of GIS. Spatial prediction factors of HSDs were selected, and the stabilities of the 147 highway slopes in Boshan District were determined. The spatial prediction model of HSDs was established by ECG-CNN, and the spatial susceptibility map of HSDs in Boshan District was plotted. Influences of the prediction factor combinations and the drill sample and verification sample combinations on the prediction success rates were verified. The results show that low susceptible areas, medium susceptible areas and high susceptible areas account for 56.92%, 28.46% and 14.62% of the total areas of Boshan District, respectively. Some sections of Binlai Expressway, G205, G309, S210 and S307, pass through high susceptible areas. The surface cutting depth has a small impact on the prediction success rate, while the elevation and gradient have great impacts on the prediction success rate. When the drill samples are small, network drill’s maturity has a great impact on the prediction success rate, while when there are many drill samples, the model’s logical structure itself has a large impact on the prediction success rate.