A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data

Liu, Yong; Chen, Zhe; Hu, Baodan; Jin, JingKun; Wu, Zhao Xia

doi:10.1007/s10064-018-1388-1

Cited by 21 publications

(7 citation statements)

References 25 publications

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“…As the result, a multipoint dataset of mean velocity values in millimetres was produced representing the deformations during the whole study period, 156 days, and the resulted data was exported to an ESRI shapefile in a point feature format in order to be processed after that in ArcGIS. In order to obtain a continuous spatial extent for the mean velocity values of ground-surface deformations and to create the highmoderate-low zonation areas map required with the total ground-surface deformation map for ranking the intensity of the ground-surface deformations in the study area during the study period, interpolation process of the velocity values was performed through inverse distance weighted (IDW) interpolation method since the multipoint dataset of the mean velocity values is interconnected dataset in the spatiotemporal domain (Liu et al, 2019).…”

Section: Landslide Hazard Mappingmentioning

confidence: 99%

Integration of Gis and Advanced Remote Sensing Techniques for Landslide Hazard Assessment: A Case Study of Northwest Syria

Hammad

Leeuwen

Mucsi

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Landslide susceptibility and hazard mapping has been developed providing remarkable results through the integration of geographic information system (GIS) and remote sensing. In this regard, some approaches have considered the use of Sentinel-1 data and time-series interferometric synthetic aperture radar (InSAR) techniques, such as differential InSAR (D-InSAR) and persistent scatterers interferometric (PSI), for providing precise information about total amount and velocity of ground-surface deformations and landslides within a specific area during a specific time period which is important for disaster management’s planning process.In this paper, artificial neural network (ANN) was used as a statistical analysis method for landslide susceptibility mapping in Northwest Syria using multi-layer perceptron (MLP) neural network on a training dataset of one dependent variable (landslide or non-landslide) and nine independent variables (slope, aspect, curvature, land cover, NDVI, lithology, distance from faults, distance from road, distance from stream networks). The resulting map of landslide susceptibility was validated using area under curve (AUC) analysis using a testing dataset which showed 90.28% of AUC value. Then, landslide susceptibility map was reclassified into high-moderate-low classes and integrated with intensity map of mean velocity of ground-surface deformations during the time period form (16 October 2018) until (21 March 2019) by using a landslide hazard matrix in a GIS environment in order to get landslide hazard map of the study area for that time period. The result shows that around 44.4%, 52.9% and 2.5% of total study area was classified as a high, moderate and low hazard zone of landslide, respectively.

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Section: Landslide Hazard Mappingmentioning

confidence: 99%

Integration of Gis and Advanced Remote Sensing Techniques for Landslide Hazard Assessment: A Case Study of Northwest Syria

Hammad

Leeuwen

Mucsi

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, the precondition of establishing a mathematical model is to ensure the integrity and validity of monitoring data. In the process of landslide monitoring, the loss or abnormality of monitoring data caused by monitoring equipment failure or external factors is inevitable [25]. It is essential for irregular data to know whether it is caused by disturbance, gear disappointment, or avalanche distortion to avoid triggering a false alarm.…”

Section: Introductionmentioning

confidence: 99%

“…Owing to the complex geologic structure of the landslides, the deformation of the monitored points at different locations is closely related to the geological features of those locations [25]. This paper provides insights into the types of landslides and the relationship between rainfall and other monitoring data through the analysis of the Zhutoushan landslide monitoring data in China and explores how to evaluate the outlier data using EDA.…”

Section: Introductionmentioning

confidence: 99%

Shock and Vibration of Rainfall on Rotational Landslide and Analysis of Its Deformation Characteristics

Liu

Chen

et al. 2021

Geofluids

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Earthquake, flood, human activity, and rainfall are some of the trigger factors leading to landslides. Landslide monitoring data analysis indicates the deformation characteristics of landslides and helps to reduce the threat of landslide disasters. There are monitoring methods that enable efficient acquisition of real-time data to facilitate comprehensive research on landslides. However, it is challenging to analyze large amounts of monitoring data with problems like missing data and outlier data during data collection and transfer. These problems also hinder practical analysis and determination concerning the uncertain monitoring data. This work analyzes and processes the deformation characteristics of a rainfall-induced rotational landslide based on exploratory data analysis techniques. First, we found that the moving average denoising method is better than the polynomial fitting method for the repair and fitting of monitoring data. Besides, the exploratory data analysis of the Global Navigation Satellite System (GNSS) monitoring data reveals that the distribution of GNSS monitoring points has a positive correlation with the deformational characteristics of a rotational landslide. Our findings in the subsequent case study indicate that rainfalls are the primary trigger of the Zhutoushan landslide, Jiangsu Province, China. Therefore, this method provides support for the analysis of rotational landslides and more useful landslide monitoring information.

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“…In view of this, some scholars have proposed a third kind of the spatio-temporal interpolation method which considers both the temporal correlations and the spatial correlations. Liu et al [24] adopted an uneven spatio-temporal Kriging interpolation algorithm to analyze the landslide displacement. Lenda et al [25] applied four different interpolation algorithms to build a surface model of the slope to analyze the progressive movement of the landslide.…”

Section: Introductionmentioning

confidence: 99%

An Improved Spatio-Temporal Kriging Interpolation Algorithm and Its Application in Slope

et al. 2020

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Slope stability analysis based on the deformation monitoring data has been commonly used to predict and warn slope disasters. However, due to breakdown of the monitoring equipment or restrictions and interferences of internal and external factors in the area, the loss of data is unavoidable during the process of the slope monitoring. This problem can be solved by the spatio-temporal Kriging interpolation algorithm. However, the subjective factors and theoretical semi-variogram of variogram models, and many parameters estimation may lead to low interpolation precision and poor calculation efficiency. In this paper, a hybrid spatio-temporal interpolation algorithm was presented by combining the improved adaptive genetic algorithm (IAGA) with the spatio-temporal Kriging interpolation method, and it was applied to monitor the deformation of HP1 slope in Yuebao open-pit mine. The results showed that the interpolation precision of the proposed method was about 1 times higher than that of the traditional spatio-temporal Kriging method and the spatio-temporal method of Gaussian process regression method. In addition, after the interpolation analysis of the missing part of the monitoring data, the maximum cumulative displacement of all monitoring points was around 35.8 mm, and the rate of the displacement deformation didn't exceed 2 mm/d for three consecutive days. The deformation trend was basically consistent with the actual slope. It shows that the improved spatio-temporal Kriging interpolation algorithm (ISTKIA) has certain feasibility and reliability, and could provide a new idea for the research of related problems in related fields. INDEX TERMS Slope deformation monitoring, spatio-temporal Kriging interpolation, ISTKIA, stability analysis.

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A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data

Cited by 21 publications

References 25 publications

Integration of Gis and Advanced Remote Sensing Techniques for Landslide Hazard Assessment: A Case Study of Northwest Syria

Integration of Gis and Advanced Remote Sensing Techniques for Landslide Hazard Assessment: A Case Study of Northwest Syria

Shock and Vibration of Rainfall on Rotational Landslide and Analysis of Its Deformation Characteristics

An Improved Spatio-Temporal Kriging Interpolation Algorithm and Its Application in Slope

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