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.
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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Cited
13 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. 10.1007/s10346-023-02153-0
- 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. 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. 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 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. 10.1007/s11629-023-7905-0
- Introduction and testing of a cost-effective GNSS system for landslide monitoring I. Berru Garcia et al. 10.1007/s11069-024-06536-x
- Dynamic–static model for monitoring wastewater treatment processes H. Han et al. 10.1016/j.conengprac.2022.105424
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. 10.1007/s10346-024-02293-x
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. 10.1016/j.enggeo.2022.106832
- Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement D. Bai et al. 10.1007/s12665-022-10637-w
- Behavior of Axially and Eccentrically Loaded Trapezoidal Shell Footings Resting on a Granular Assembly S. Sharifi et al. 10.1061/(ASCE)GM.1943-5622.0002442
- Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification R. He et al. 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. 10.1007/s10346-024-02237-5
13 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. 10.1007/s10346-023-02153-0
- 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. 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. 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 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. 10.1007/s11629-023-7905-0
- Introduction and testing of a cost-effective GNSS system for landslide monitoring I. Berru Garcia et al. 10.1007/s11069-024-06536-x
- Dynamic–static model for monitoring wastewater treatment processes H. Han et al. 10.1016/j.conengprac.2022.105424
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. 10.1007/s10346-024-02293-x
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. 10.1016/j.enggeo.2022.106832
- Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement D. Bai et al. 10.1007/s12665-022-10637-w
- Behavior of Axially and Eccentrically Loaded Trapezoidal Shell Footings Resting on a Granular Assembly S. Sharifi et al. 10.1061/(ASCE)GM.1943-5622.0002442
- Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification R. He et al. 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. 10.1007/s10346-024-02237-5
Latest update: 07 Jan 2025
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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