Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 13; p. 2483
• Main Authors: Chen, Zi, Huang, Guanwen, Zhang, Yongzhi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Adaptive sampling; Algorithms; AMPSO-SVR; Artificial neural networks; Back propagation networks; China; Deformation; Deformation effects; disaster preparedness; Disasters; displacement prediction; Emergency preparedness; expansive soil landslide; Expansive soils; Goodness of fit; hysteresis; Landslides; Landslides & mudslides; Machine learning; model validation; Monitoring; multiple driven factors; mutation; Neural networks; Noise prediction; Optimization; Particle swarm optimization; Polynomials; prediction; Predictions; Regression analysis; Regression models; Root-mean-square errors; soil; Support vector machines; Time lag; time lag effect; Time series; Trends; China

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a supp...