Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-411-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-411-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2022
Research article |
| 14 Feb 2022
| 14 Feb 2022
Evaluation of filtering methods for use on high-frequency measurements of landslide displacements
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada
Michael T. Hendry
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada
Renato Macciotta
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada
Trevor Evans
Canadian National Railway, Kamloops, BC, Canada
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Total article views: 3,634 (including HTML, PDF, and XML)
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Total article views: 2,871 (including HTML, PDF, and XML)
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Total article views: 763 (including HTML, PDF, and XML)
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Cited
19 citations as recorded by crossref.
- Evaluating topography-based methods in 3D decomposition of InSAR 1D velocities obtained for translational landslides: Thompson River valley in Canada S. Sharifi et al. https://doi.org/10.1007/s10346-023-02153-0
- Research and development of VR system for training vocal music in Vietnam L. Thai et al. https://doi.org/10.1016/j.cexr.2026.100154
- A novel approach for underwater acoustic signal denoising based on improved time-variant filtered empirical mode decomposition and weighted fusion filtering G. Li et al. https://doi.org/10.1016/j.oceaneng.2024.119550
- Precursory Motion and Time‐Of‐Failure Prediction of the Achoma Landslide, Peru, From High Frequency PlanetScope Satellites P. Lacroix et al. https://doi.org/10.1029/2023GL105413
- An improved estimation of surficial velocities obtained by MT-TOPSAR interferometry: a case study of Oldman River Dam, Alberta, Canada S. Sharifi & M. Hendry https://doi.org/10.1007/s10064-023-03473-0
- A framework for identifying the onset of landslide acceleration based on the exponential moving average (EMA) J. Wang et al. https://doi.org/10.1007/s11629-023-7905-0
- Reconstruction and forecasting of slow-moving landslide displacement using a Kalman Filter approach M. Mishra et al. https://doi.org/10.5194/nhess-26-1621-2026
- Introduction and testing of a cost-effective GNSS system for landslide monitoring I. Berru Garcia et al. https://doi.org/10.1007/s11069-024-06536-x
- Dynamic–static model for monitoring wastewater treatment processes H. Han et al. https://doi.org/10.1016/j.conengprac.2022.105424
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. https://doi.org/10.1007/s10346-024-02293-x
- Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control R. Naagarajan et al. https://doi.org/10.3389/fagro.2025.1536998
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. https://doi.org/10.1016/j.enggeo.2022.106832
- A Novel LOF–KNN–Bessel Approach for Optimizing and Predicting Slope Deformation Monitoring Data: A Case Study of the Shilu Iron Mine C. Ma et al. https://doi.org/10.3390/math14112012
- Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement D. Bai et al. https://doi.org/10.1007/s12665-022-10637-w
- Behavior of Axially and Eccentrically Loaded Trapezoidal Shell Footings Resting on a Granular Assembly S. Sharifi et al. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002442
- Accelerating growth of Sermilik Delta, Greenland (1987–2022), driven by increasing runoff R. Crick et al. https://doi.org/10.1002/esp.70116
- Noise reduction method of underwater acoustic signal based on trend perception optimization and double-level classification G. Li et al. https://doi.org/10.1016/j.measurement.2026.121944
- Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification R. He et al. https://doi.org/10.3390/pr11020436
- Critical assessment of landslide failure forecasting methods with case histories: a comparative study of INV, MINV, SLO, and VOA S. Sharifi et al. https://doi.org/10.1007/s10346-024-02237-5
19 citations as recorded by crossref.
- Evaluating topography-based methods in 3D decomposition of InSAR 1D velocities obtained for translational landslides: Thompson River valley in Canada S. Sharifi et al. https://doi.org/10.1007/s10346-023-02153-0
- Research and development of VR system for training vocal music in Vietnam L. Thai et al. https://doi.org/10.1016/j.cexr.2026.100154
- A novel approach for underwater acoustic signal denoising based on improved time-variant filtered empirical mode decomposition and weighted fusion filtering G. Li et al. https://doi.org/10.1016/j.oceaneng.2024.119550
- Precursory Motion and Time‐Of‐Failure Prediction of the Achoma Landslide, Peru, From High Frequency PlanetScope Satellites P. Lacroix et al. https://doi.org/10.1029/2023GL105413
- An improved estimation of surficial velocities obtained by MT-TOPSAR interferometry: a case study of Oldman River Dam, Alberta, Canada S. Sharifi & M. Hendry https://doi.org/10.1007/s10064-023-03473-0
- A framework for identifying the onset of landslide acceleration based on the exponential moving average (EMA) J. Wang et al. https://doi.org/10.1007/s11629-023-7905-0
- Reconstruction and forecasting of slow-moving landslide displacement using a Kalman Filter approach M. Mishra et al. https://doi.org/10.5194/nhess-26-1621-2026
- Introduction and testing of a cost-effective GNSS system for landslide monitoring I. Berru Garcia et al. https://doi.org/10.1007/s11069-024-06536-x
- Dynamic–static model for monitoring wastewater treatment processes H. Han et al. https://doi.org/10.1016/j.conengprac.2022.105424
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. https://doi.org/10.1007/s10346-024-02293-x
- Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control R. Naagarajan et al. https://doi.org/10.3389/fagro.2025.1536998
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. https://doi.org/10.1016/j.enggeo.2022.106832
- A Novel LOF–KNN–Bessel Approach for Optimizing and Predicting Slope Deformation Monitoring Data: A Case Study of the Shilu Iron Mine C. Ma et al. https://doi.org/10.3390/math14112012
- Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement D. Bai et al. https://doi.org/10.1007/s12665-022-10637-w
- Behavior of Axially and Eccentrically Loaded Trapezoidal Shell Footings Resting on a Granular Assembly S. Sharifi et al. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002442
- Accelerating growth of Sermilik Delta, Greenland (1987–2022), driven by increasing runoff R. Crick et al. https://doi.org/10.1002/esp.70116
- Noise reduction method of underwater acoustic signal based on trend perception optimization and double-level classification G. Li et al. https://doi.org/10.1016/j.measurement.2026.121944
- Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification R. He et al. https://doi.org/10.3390/pr11020436
- Critical assessment of landslide failure forecasting methods with case histories: a comparative study of INV, MINV, SLO, and VOA S. Sharifi et al. https://doi.org/10.1007/s10346-024-02237-5
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
This study is devoted to comparing the effectiveness of three different filters for noise reduction of instruments. It was observed that the Savitzky–Golay and Gaussian-weighted moving average filters outperform the simple moving average. Application of these two filters in real-time landslide monitoring leads to timely detection of acceleration moment and better preservation of information regarding displacement and velocity.
This study is devoted to comparing the effectiveness of three different filters for noise...
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