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
• DOI: 10.3390/rs16132483
• ISSN: 2072-4292

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 support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.