Academic Journal of Environment & Earth Science, 2023, 5(1); doi: 10.25236/AJEE.2023.050109.
Ren Dan, Jia Zhiqiang
College of Earth Science, Guilin University of Technology, Guilin, China
It is crucial to undertake thorough study and data mining on factors that determine the occurrence of geohazards to prevent and anticipate their occurrence. A small-sample learning method with a strong theoretical underpinning, the classical support vector machine model provides considerable expressive power in dealing with the interaction of nonlinear characteristics and lessens reliance on the entire data set. However, its excellent generalization capabilities result in an excessively large optimal search space, which impacts the search for kernel parameters and reduces the model’s accuracy. The particle swarm algorithm, which has a robust search capability, is thus introduced to improve it. This paper's research area is Chian Town, China. The prediction study of geological hazard susceptibility in Chian town was validated using Gaofen 2 remote sensing imagery in conjunction with a support vector machine regression model enhanced by the particle swarm algorithm. The findings show that the high-risk zone comprises 17% of the overall area and has an 88.88% prediction accuracy. The results imply that integrating high-resolution remote sensing imagery-based and optimized machine learning algorithm models has prospective applications in investigating geological hazard susceptibility with small samples, numerous feature factors, and large-scale data.
High-resolution remote sensing, PSO-SVR, Geological hazard
Ren Dan, Jia Zhiqiang. Geological Hazard Susceptibility Assessment Based on RS and PSO-SVR Model. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 1: 62-70. https://doi.org/10.25236/AJEE.2023.050109.
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