Time and path prediction of landslides using InSAR and flow model

Roy, Priyom; Martha, Tapas R.; Khanna, Kirti; Jain, Namrata; Kumar, K. Vinod

doi:10.1016/j.rse.2022.112899

Cited by 42 publications

(13 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the early-warning monitoring techniques available in the literature are generally aimed at predicting specific events for which an analytical or semi-empirical model is known. For example, the instant in which a landslide will occur was predicted in [56] through the inverse speed method, while the failure mechanism of a cataloged landslide event was studied in [57] to forecast the occurrence of similar events in different geographical areas according to some influencing factors. An attempt to predict the collapse of large infrastructures was also made in [28] using deformation outliers.…”

Section: Discussionmentioning

confidence: 99%

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

Montisci

Porcu

2022

Applied Sciences

View full text Add to dashboard Cite

Structural failure prevention is a crucial issue in civil engineering. The causes of structure or infrastructure collapse include phenomena that slowly deform the ground and could affect the stability of foundations such as differential settlements, subsidence, groundwater changes, slope failure, or landslides. When large urban areas need to be monitored, such phenomena are hard to be mapped by means of classical structural health monitoring methods due to the unaffordable quantity of in situ measurements these methods would entail. A very effective alternative is exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries which would enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. Analyzing the enormous amount of data produced by MT-InSAR may help to assess the time evolution of phenomena but can barely highlight “anomalous” ground deformations in time, to prevent likely structural failure. This paper proposes a method which analyzes the InSAR data through an unsupervised learning paradigm with the purpose of detecting critical events at their early stage. On the basis of similarities among time sequences, this method allows the finding of precursors of anomalous ground settlement behaviors, the correct framing of which should be directed to specialist evaluation and in situ inspections.

show abstract

Section: Discussionmentioning

confidence: 99%

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

Montisci

Porcu

2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…InSAR technology has emerged in recent years for the early identification of geological hazards due to its robustness to various weather conditions, wide monitoring range, and high accuracy, which makes it widely applicable in landslide detection research [ 50 , 51 ]. However, the motion of slopes is three-dimensional in space.…”

Section: Methodsmentioning

confidence: 99%

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Zhu

Gan

Yuan

et al. 2022

Sensors

View full text Add to dashboard Cite

Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides.

show abstract

“…Figure 3 shows the time position of ascending track. Permanent scatterer interferometry is an advanced multi-InSAR technology that is widely used in ground deformation monitoring, and its results can reach millimeter displacement in monitoring accuracy (Ferretti, 2001;Liu et al, 2020;Roy et al, 2022).…”

Section: Sentinel-1a Datasets and Ps-insat Technologymentioning

confidence: 99%

Landslide susceptibility mapping based on the deformation intensity

Jin,

Zeng,

Yin

et al. 2023

Preprint

View full text Add to dashboard Cite

Affected by the human engineering activities and extreme climate change, landslide disasters develop frequently in the channel of the Three Gorges Reservoir Area. The framework related to the extension of dynamic susceptibility modeling has largely not been explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, which a transportation channel with significant economic value to carry out innovative research in two stages: (i) five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF) and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries; (ii) The PS-InSAR-based intensify factor was generated by the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. Then the intensify factor was combined with the proposed static factors and machine learning models to generate enhanced landslide susceptibility mapping (ELSM). The area under the receiver operating characteristic curve (AUC) was proposed as the evaluation indicator. Dynamic landslide susceptibility mapping has improved model accuracy, especially with DT models achieving 2% enhancement and the highest AUC value of 93.1%. The susceptibility results of introducing intensify factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

show abstract

Time and path prediction of landslides using InSAR and flow model

Cited by 42 publications

References 70 publications

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Landslide susceptibility mapping based on the deformation intensity

Contact Info

Product

Resources

About