Displacement Datasets Research Articles

Nondestructively identifying the mechanical behavior of soft tissues using surface deformation with an explicit inverse approach

Identifying the spatial variation of stiffness properties in soft tissues nondestructively, with limited surface measurements, poses significant challenges. In this paper, we present a novel explicit inverse approach designed to characterize the nonhomogeneous elastic property distribution of soft tissues using only surface displacement datasets. In contrast to the prevalent implicit inverse approach, which focuses on optimizing the elastic properties of individual pixels, our proposed method optimizes the geometric parameters of deformable and movable components, as well as shear moduli of each component. As a result, the proposed approach requires far fewer optimization variables, streamlining the process. Numerical tests conducted in this study demonstrate the superiority of the explicit inverse method over the implicit inverse method, providing much-improved reconstructed results. In particular for a ring structure, while the average relative error of reconstruction using the implicit inverse method can exceed 40 %, the explicit inverse method achieves a remarkable average relative error of only 5 %. Given that surface displacements are easily measurable, the integration of our proposed explicit inverse method with low-cost imaging techniques shows great potential in accurately mapping the elastic property distribution of biological tissues.

Transforming Landslide Prediction: A Novel Approach Combining Numerical Methods and Advanced Correlation Analysis in Slope Stability Investigation

Landslides cause significant economic losses and casualties worldwide. However, robust prediction remains challenging due to the complexity of geological factors contributing to slope stability. Advanced correlation analysis methods can improve prediction capabilities. This study aimed to develop a novel landslide prediction approach that combines numerical modeling and correlation analysis (Spearman rho and Kendall tau) to improve displacement-based failure prediction. Simulations generate multi-location displacement data sets on soil and rock slopes under incremental stability reductions. Targeted monitoring points profile local displacement responses. Statistical analyses, including mean/variance and Spearman/Kendall correlations, quantified displacement-stability relationships. For the homogeneous soil slope, monitoring point 2 of the middle section of the slope showed a mean horizontal displacement of 17.65 mm and a mean vertical displacement of 9.72 mm under stability reduction. Spearman’s rho correlation coefficients ranged from 0.31 to 0.76, while Kendall’s tau values ranged from 0.29 to 0.64, indicating variable displacement–stability relationships. The joint rock slope model had strong positive total displacement correlations (Spearman’s and Kendall’s correlation ranges of +1.0 and −1.0) at most points. Horizontal and vertical displacements reached mean maxima of 44.13 mm and 22.17 mm, respectively, at the unstable point 2 of the center section of the slope. The advanced correlation analysis techniques provided superior identification of parameters affecting slope stability compared to standard methods. The generated predictive model dramatically improves landslide prediction capability, allowing preventive measures to be taken to mitigate future losses through this new approach.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Unfamiliarity generates costly aggression in interspecific avian dominance hierarchies

Dominance hierarchies often form between species, especially at common feeding locations. Yet, relative to work focused on the factors that maintain stable dominance hierarchies within species, large-scale analyses of interspecific dominance hierarchies have been comparatively rare. Given that interspecific behavioral interference mediates access to resources, these dominance hierarchies likely play an important and understudied role in community assembly and behavioral evolution. To test alternative hypotheses about the formation and maintenance of interspecific dominance hierarchies, we employ an large, participatory science generated dataset of displacements observed at feeders in North America in the non-breeding season. Consistent with the hypothesis that agonistic interference can be an adaptive response to exploitative competition, we find that species with similar niches are more likely to engage in costly aggression over resources. Among interacting species, we find broad support for the hypothesis that familiarity (measured as fine-scale habitat overlap) predicts adherence to the structure of the dominance hierarchy and reduces aggression between species. Our findings suggest that the previously documented agonistic hierarchy in North American birds emerges from species-level adaptations and learned behaviors that result in the avoidance of costly aggression.

A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety.

Learning-based acoustic displacement field modeling and micro-particle control

Acoustic manipulation has emerged as a promising microtarget control method with potential applications in microchip assembly, noncontact control of chemical materials, and cell control. However, the precision of acoustic micro-/nano-manipulation is constrained by the ambiguity of the acoustic displacement field. The nonlinear properties of displacement fields cannot be appropriately simulated using conventional mathematical fitting techniques. In this study, considering the complexity of particle motion on a thin plate driven by acoustic waves, the original displacement dataset was collected based on an experimental platform and was then trained using a neural network to model the displacement of unknown points on the plate. The corresponding cost functions were designed based on a manipulation task and particle number to determine the frequency sequence and achieve motion control of micro/nano targets by selecting the driving frequency with the lowest cost during the controlling process. Based on the above predictions and control effects, the acoustic manipulation platform constructed in this study could be used to manipulate the linear motion of a single particle and control multiple particle encounters as well as to manipulate the complex trajectory “NK”. This work opens up an accessible path for an acoustic displacement field and enables achievable microtarget motion control.

LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter

Monitoring landslide displacement is critical towards understanding kinematics and evaluating risk. Both remote sensing technology and in-situ sensors are currently used for monitoring purposes, each with their own spatio-temporal advantages. However, the majority of current workflows analyze these data independently, missing a synergistic opportunity to combine complementary datasets to enhance analyses both spatially and temporally. This work presents the first landslide specific displacement interpolation technique using a novel approach, LADI (Landslide Displacement Interpolation), which creates a high-spatial, high-temporal resolution interpolation of landslide surface displacement by combining remotely-sensed observation data with and in-situ sensor data. At a minimum, LADI requires only a single in-situ monitoring station (control point) and a single high-resolution displacement dataset (calibration data) to function. LADI implements a series of Kalman filters, where interpolation is first performed spatially, then optionally in the temporal domain. LADI is demonstrated on both simulated and real-world landslide data to showcase the behavior, effectiveness, and applicability of the method. Results are compared to common spatial interpolation methods, which demonstrate that LADI achieves superior performance (average EPE = 0.018 m and 0.031 m of LADI and the best performing common spatial interpolation method respectively when number of interpolation control points = 4) in the real-world test sparse displacement dataset when the number of spatial control points is less than sixteen. Additionally, the performance gain of utilizing LADI is further enhanced over common interpolation methods when the interpolation control points are not equally distributed geographically (average EPE = 0.016 m and 0.049 m respectively) as is usually the case with in-situ landslide displacement monitoring stations.

An approach for prospective forecasting of rock slope failure time

Rock slope failures globally account for most single-event landslide disasters. Climatic changes in mountain areas boost failure activity and the demand for reliable failure time forecasts. State-of-the-art prediction models are often confused with high-frequency slope deformation data. Prospectively, they provide ambiguous forecasts as data filtering, starting point definition and forecast uncertainty remain arbitrary. Here, we develop a prospective failure time forecast model that applies multiple filtering and inverse velocity percentiles to minimize subjective decisions. We test the concept with 14 historic slope failures of 102-108 m3 including 46 displacement datasets from different sensors. After automatic detection of the onset of acceleration, the failure time of all events is forecasted to within −1 ± 17 h for higher-frequency data and −1 ± 4 d for daily data with a final mean uncertainty of 1 ± 1 d and 7 ± 4 d that is estimated in real-time. This prospective approach overcomes previous long-standing problems by introducing a robust and uniform concept across various types of catastrophic slope failures and sensors.

Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

Deep learning for predicting frequency lock-in of a freely vibrating sphere

In this paper, we present a deep learning-based reduced-order model (DL-ROM) for the stability prediction of unsteady three-dimensional fluid–structure interaction systems. The proposed DL-ROM has the format of a nonlinear state-space model and employs a recurrent neural network with long short-term memory (LSTM) cells. We consider a canonical fluid–structure system of an elastically mounted sphere coupled with the incompressible fluid flow in a state-space format. Specifically, we develop a nonlinear data-driven coupling for predicting the unsteady forces and the vortex-induced vibration (VIV) lock-in of the freely vibrating sphere in a transverse direction. We design an input–output relationship as a temporal sequence of force and displacement datasets for a low-dimensional approximation of the fluid–structure system. Based on the prior knowledge of the VIV lock-in process, the input function contains a range of frequencies and amplitudes, which significantly affects the system realization ability and enables an efficient DL-ROM without the need for a massive training dataset for the low-dimensional modeling. Once trained, the network provides a nonlinear mapping of input–output dynamics that can predict the coupled fluid–structure dynamics for a longer horizon via the feedback process. By integrating the LSTM network with the eigensystem realization algorithm (ERA), we construct a data-driven state-space model for the reduced-order stability analysis. We investigate the underlying mechanism and stability characteristics of VIV of a freely vibrating sphere via an eigenvalue selection process. Using the reduced-order eigenvalue analysis, we characterize the vibrating sphere-wake lock-in phenomenon and demonstrate that the lock-in responds at preferred vibration frequencies. To understand the frequency lock-in mechanism, we study the eigenvalue trajectories for a range of the reduced oscillation frequencies and the mass ratios. Consistent with the full-order simulations, the frequency lock-in branches are accurately captured by the combined LSTM-ERA procedure. The proposed DL-ROM aligns with the development of physics-based digital twin of engineering systems involving moving boundaries and fluid–structure interactions.

Constraining Paleoseismicity of the Wulashan Piedmont Fault on the Northern Margin of the Ordos Block From Fault Scarp Morphology

Fault scarps preserve important information about past earthquakes on a fault, and thus can be applied to investigate the fault slip histories and rupture patterns. In this study, the morphology of fault scarps was used to constrain the paleoseismicity of the Wulashan Piedmont Fault located on the northern margin of the Ordos Block based on high-resolution LiDAR topography. We constructed the vertical displacement distribution of the fault through measuring the heights of a large number of scarp profiles extracted on different geomorphic surfaces along the fault. Through statistical analysis of the dense collection of vertical displacement dataset, a total of seven paleoseismic events were identified which followed a characteristic slip pattern with an average slip of ∼1.0 m. We further detected slope breaks in the fault scarp morphology to quantify the number of paleoearthquakes that occurred on the scarps, and discriminated at least five individual surface-breaking events. Both the number and slip of paleoearthquakes recognized from the morphology of fault scarps were in good agreement with previous paleoseismic trenching records. Based on the empirical scaling relationship between moment magnitude and rupture parameters, a moment magnitude ofMw6.7–7.5 was determined for the paleoearthquakes occurred on the fault. With the fault slip rate derived by previous studies, we estimated an average recurrence interval of 1.3–1.8 kyr for the paleoseismic events, which is very close to the elapsed time since the most recent earthquake, indicating a high potential seismic hazard on the Wulashan Piedmont Fault.

Quantifying stability of parameter estimates for in vivo nearly incompressible transversely-isotropic brain MR elastography

Easily computable quality metrics for measured medical data at point-of-care are important for imaging technologies involving offline reconstruction. Accordingly, we developed a new data quality metric for in vivo transversely-isotropic (TI) magnetic resonance elastography (MRE) based on a generalization of the widely accepted octahedral shear-strain calculation. The metric uses MRE displacement data and an estimate of the TI property field to yield a ‘stability map’ which predicts regions of low versus high accuracy in the resulting material property reconstructions. We can also calculate an average TI parameter stability (TIPS) score over all voxels in a region of interest for a given measurement to indicate how reliable the recovered mechanical property estimate for the region is expected to be. The calculation is rapid and places little demand on computing resources compared to the computationally intensive material property reconstruction from non-linear inversion (TI-NLI) of displacement fields, making it ideal for point-of-care evaluation of data quality. We test the predictions of the stability map for both simulated phantoms and in vivo human brain data. We used a range of different displacement datasets from vibrations applied in the anterior-posterior (AP), left-right (LR) and combined AP + LR directions. The TIPS and variability maps (noise sensitivity or variation from the mean of repeated MRE scans) were consistently anti-correlated. Notably, Spearman correlation coefficients ∣R∣>0.6 were found between variability and TIPS score for individual white matter tracts with in vivo data. These observations demonstrate the reliability and promise of this data quality metric to screen data rapidly in realistic clinical MRE applications.

Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model

In this work we propose Support Vector Machine classification algorithms to classify one-dimensional crystal lattice waves from locally sampled data. Different learning datasets of particle displacements, momenta and energy density values are considered. Efficiency of the classification algorithms is further improved by two dimensionality reduction techniques: Principal Component Analysis and Locally Linear Embedding. Robustness of classifiers is investigated and demonstrated. Developed algorithms are successfully applied to detect localized intrinsic modes in three numerical simulations considering a case of two localized stationary breather solutions, a single stationary breather solution in noisy background and two mobile breather collision.

Stratified mobility fishery models with harvesting outside of no-take areas

Mobility stratification, identifiable from k-means clustering on an appropriate displacement data set, is a common feature of many fish species wherein distinct low-mobility ‘station-keeper’ and high-mobility ‘ranger’ types are recognized. From recapture records of speckled snapper Lutjanus rivulatus, we develop a Gaussian mixture model of the probability density function for random displacements by the two types. This leads to a system of two coupled reaction-diffusion equations. We consider a single no-take area (NTA) in one and two dimensions containing a mobility-structured species. The minimum size of this NTA that leads to species survival is derived and then generalised to a population with n mobility types. Exact non-uniform 1-D steady states are constructed for the full nonlinear mobility-structured model with lethal (zero density boundary condition) harvesting outside of the NTA. This model is then extended to include an array of evenly spaced NTAs with a bounded harvesting rate allowed between them. The minimum size of linear, circular and annular NTAs and the maximum sizes of the surrounding fractionally harvested zones that ensure species survival and connectivity are calculated.

Differential resistance based self-sensing recurrent neural network model for position estimation and control of antagonistic Shape Memory Alloy actuator

The paper presents the development and experimental investigation of recurrent neural network (RNN) based self-sensing position estimation (SSPE) model for shape memory alloy actuator (SMA). RNN is used as an estimator in the position feedback control loop to replace the external additional position sensor. The model was inspired by the physics-based analogy of the Mass-Spring-Damper (MSD) system for the antagonistic SMA wire actuator. Actuator displacement presents the hysteresis and non-linear dynamic relationship with observed differential resistance (sensing signal) during phase transformation. The resistance variation causes due to dissimilar resistivity between primary phases of SMA material. The RNN based estimation model was considered because it consists of memory elements for storing the processed information and feedback connections for dynamic modeling of the system. RNN was trained with input sensing signal and target displacement datasets. The estimation accuracy of the model was real-time evaluated during trajectory tracking of reference signals in the feedback control loop. A quantitative performance analysis is assessed in terms of correlation (ℛ), mean absolute error (MEA), and root mean square error (RSME) of the learned model along with the developed actuator system. The tracking results confirm the close agreement between estimated and measured displacement at a reasonable accuracy.

Global Drivers and Transport Mechanisms of Lunar Rockfalls

AbstractThe long‐ and short‐term drivers and transport mechanisms of lunar rockfalls are currently not well understood, but could provide valuable information about the geologic processes that still shape the surface of the Moon today. Here, we compare the global distribution of rockfalls with relevant geophysical data, such as seismic, topographic, thermal, gravity anomaly, and tidal displacement data sets. Rockfalls appear to predominantly occur (a) on equator‐facing slopes and thus in regions with large thermal amplitudes, (b) on slope angles well above‐average (Δ ∼ 10°), and (c) in regions with above‐average rock abundance. We do not observe a qualitatively or statistically relevant relation between rockfall abundance, monitored Apollo‐era shallow seismic activity, and the distribution of visible tectogenetic features. Informed by our global analysis, we conduct a targeted, in‐depth study of 687 rockfall boulders and trajectories in 13 sites across the Moon, including 7 craters, 2 volcanic vents, 2 tectonic structures, and 2 unclassified geomorphic regions. We identify four different source region types, where the type appears to control the occurrence of rockfalls. The source region type in turn is controlled by surface age rather than geomorphic context. We find that rockfall trajectories are mainly controlled by the trigger energy and the geometry of the slope. Our results suggest that erratic small‐scale impacts (mainly in old, Imbrian‐Nectarian, shallow terranes), aided by solar‐induced thermal fatigue of fractured bedrock (mainly in young, Copernican‐Eratosthenian steep terranes), were the dominant, global‐scale long‐ and short‐term drivers of rockfalls in the Moon's recent geologic past.

Three-Dimensional Surface Displacement of the Eastern Beijing Plain, China, Using Ascending and Descending Sentinel-1A/B Images and Leveling Data

Surface displacement is an common environmental geological phenomenon in the Beijing Plain. Research on surface displacement in the Beijing Plain has mainly focused on vertical surface displacement, whereas the horizontal displacement has scarcely been studied. To investigate the 3-D surface displacement in the Beijing Plain, we construct a leveling-constrained multidirectional PS-InSAR 3-D surface displacement estimation method to obtain the 3-D surface displacement information. The results show that the surface displacement in the study area during 2016–2018 was mainly vertical displacement with two main northern and southern subsidence centers; the vertical displacement ranged from −150 mm/year (down) to 5 mm/year (up), and the east–west horizontal displacement ranged from 20 mm/year (east) to 22 mm/year (west). Validation results show that the 3-D surface displacement estimation results agree well with leveling data and GPS data, indicating the reliability of the 3-D surface displacement datasets. The 3-D surface displacement results show that horizontal displacement is obvious in the areas with a large vertical displacement in the eastern Beijing Plain. Additionally, the horizontal displacement is directed toward the center of vertical displacement. The compressive strain is observed close to the centers of vertical displacement, whereas tensile strain occurs far from the centers of vertical displacement. The main cause of the 3-D surface displacement in the study area is the long-term groundwater overexploitation, especially deep groundwater exploitation. The spatial and temporal extents of displacement do not exactly match the locations of the groundwater sinks in different aquifers; instead, geological structures and stratigraphic/lithological conditions may have a combined effect. Moreover, the spatial and temporal distributions of surface displacement are closely related to ground fissure activity, and both influence each other.

CDIP observations of recent extreme wave conditions on U.S. coasts

The Coastal Data Information Program (CDIP) maintains wave gauge stations for continuous coverage, with precision instruments and dedicated telemetry and dissemination infrastructure. Decades of this persistent, quality-controlled wave monitoring effort has provided the data required to generate metrics for wave climate at coastal locations across the United States and identify and characterize extreme wave events. During the extremely active 2020 North Atlantic hurricane season, the CDIP East Coast array recorded significantly elevated wave conditions generated by no fewer than 15 named storms. In California, meanwhile, long-term monitoring stations have measured new all-time maximum wave heights during recent storm events. Complete quality-controlled directional spectra and displacement data sets, as well as sea surface temperature and surface current data from the wave buoys, are publicly available at http://cdip.ucsd.edu.

Time Series InSAR Three-Dimensional Displacement Inversion Model of Coal Mining Areas Based on Symmetrical Features of Mining Subsidence

The three-dimensional (3-D) displacements of mining areas is the basis of studying the mining subsidence law and obtaining surface movement parameters. The traditional multi-temporal interferometry synthetic aperture radar (InSAR) technology can only obtain the surface deformation in line-of-sight (LOS) direction, even if some methods can obtain the 3-D displacements of mining area based on InSAR. However, it has high data requirements for data types, which are not conducive to the inversion of 3-D displacements. In this paper, the symmetry of the surface basin caused by mining subsidence under different mining degrees is analyzed. According to the basic symmetrical features of mining subsidence—that the surface vertical displacement and horizontal displacement in near horizontal coal seam is symmetrical with respect to the main section of the basin, combined with time series InSAR technology (TS-InSAR)—a novel method for retrieving the 3-D displacement results from a single-geometry InSAR dataset based on symmetrical features (hereafter referred to as the SGI-SF method) is proposed. The SGI-SF method first generates multi-temporal observations of LOS displacement from a single-geometry InSAR dataset, and then transforms them into multi-temporal observations of 3-D displacement datasets according to symmetrical features. There is no necessity to obtain the surface movement parameters from the measured data to calculate 3-D displacement fields. Finally, the time series of 3-D displacements are estimated from multi-temporal 3-D displacements using the singular value decomposition (SVD) method. Nine descending Sentinel-1 images from the Yulin mining area of China are used to test the proposed SGI-SF method. The results show that the average root mean square errors (RMSE) in the vertical and horizontal direction of the three-dimensional deformations are approximately 9.28 mm and 13.10 mm, respectively, which are much smaller than mining-induced displacements and can provide support for deformation monitoring in mining areas.

Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization-tuned support vector machines

Accurate and reliable displacement prediction is vital for developing landslide early warning system, since displacement variations directly reveal the evolution of landslide. The past decade has seen the rapid development of displacement prediction technology. Most of the existing studies are based on deterministic point prediction models, with the goal of obtaining high-precision predictions. However, these pointwise predictions do not give any indication of credibility, which makes it difficult to make accurate decisions for subsequent landslide treatment. To address this problem, a prediction interval estimation method, instead of point prediction, for landslide displacement is proposed. In the methodology, double exponential smoothing is employed to deal with the nonlinear cumulative displacement, while two support vector machines are proposed to directly generate the displacement lower boundary and upper boundary. To obtain optimal model parameters, the adaptive chicken swarm optimization using chaotic mapping and adaptive inertia weight strategies is proposed to minimize a modified prediction interval-based objective function. A real-world landslide displacement data set is used to demonstrate the proposed method. Experimental results show that it is a promising tool for the construction of high-quality prediction intervals, which can inform the landslide treatment-related decision-making.