Methodology for Landslide Damage Assessment

Palmisano, Fabrizio; Vitone, Claudia; Cotecchia, Federica

doi:10.1016/j.proeng.2016.08.679

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…This landslide affects only the clayey-silty deposits of the clayey member of the Flysch di Faeto fm and forms a roto-translational body, almost 740 m long and 250 m wide, with toe emerging along the left-side valley of the Biletra River (Cotecchia et al 2016). The southern outskirt of the town of Bovino extends over the depletion area of the Pianello landslide, where several buildings show structural damage mostly linked to the recent activity of the landslide (Palmisano 2016;Palmisano et al 2016;Cotecchia et al 2016).…”

Section: Geological Settingmentioning

confidence: 99%

Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy)

et al. 2019

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We present an example of integration of persistent scatterer interferometry (PSI) and in situ measurements over a landslide in the Bovino hilltop town, in Southern Italy. First, a widearea analysis of PSI data, derived from legacy ERS and ENVISAT SAR image time series, highlighted the presence of ongoing surface displacements over the known limits of the Pianello landslide, located at the outskirts of the Bovino municipality, in the periods 1995-1999 and 2003-2008, respectively. This prompted local authorities to install borehole inclinometers on suitable locations. Ground data collected by these sensors during the following years were then compared and integrated with more recent PSI data from a series of Sentinel-1 images, acquired from March 2014 to October 2016. The integration allows sketching a consistent qualitative model of the landslide spatial and subsurface structure, leading to a coherent interpretation of remotely sensed and ground measurements. The results were possible thanks to the synergistic operation of local authorities and remote sensing specialists, and could represent an example for best practices in environmental management and protection at the regional scale.

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Section: Geological Settingmentioning

confidence: 99%

Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy)

et al. 2019

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“…Thereinto landslide hazards are generally considered as the most representative disasters which could induce enormous losses on residents' lives and property (Huang and Li, 2011;Palmisano et al, 2016;Huang et al, 2019). To some degree, landslide occurrence hampers the efficient utilization of land resources and economic development in mountainous regions.…”

Section: Introductionmentioning

confidence: 99%

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

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Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree (FT), support vector machine (SVM) and classification regression tree (CART) and were integrated with bagging strategy. Then these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation (CAE) method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve (ROC) and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. among them the Bag-CART model has the highest AUC value of 0.874, however the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

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“…Landslides, one of the most frequent geological hazards in China, cause thousands of millions of dollars in damage, dozens of casualties, and many geological environment problems every year [ 1 , 2 , 3 , 4 , 5 ]. In order to reduce the losses caused by landslides, predicting the areas where landslides are most likely to occur has become more important [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

et al. 2019

Entropy

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Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

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Methodology for Landslide Damage Assessment

Cited by 13 publications

References 5 publications

Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy)

Integration of persistent scatterer interferometry and ground data for landslide monitoring: the Pianello landslide (Bovino, Southern Italy)

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

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