scholarly journals The contribution of EMCA to landslide susceptibility mapping in Central Asia

2015 ◽  
Vol 58 (1) ◽  
Author(s):  
Annamaria Saponaro ◽  
Marco Pilz ◽  
Dino Bindi ◽  
Stefano Parolai
Keyword(s):  
Central Asia ◽  
Landslide Susceptibility ◽  
Negative Impact ◽  
Regional Scale ◽  
Heavy Precipitation ◽  
Landslide Hazard ◽  
Landslide Susceptibility Mapping ◽  
Large Variability ◽  
Ground Shaking ◽  
Potential Factors

<p>Central Asia is one of the most exposed regions in the world to landslide hazard. The large variability of local geological materials, together with the difficulties in forecasting heavy precipitation locally and in quantifying the level of ground shaking, call for harmonized procedures to better quantify the hazard and the negative impact of slope failures across the Central Asian countries. As a first step towards a quantitative landslide hazard and risk assessment, a landslide susceptibility analysis at regional scale has been carried out, by benefitting of novel seismic hazard outcomes reached in the frame of Earthquake Model Central Asia (EMCA) project. By combining information coming from diverse potential factors, it is possible to detect areas where a potential for landslides exists. Initial results allow the identification of areas that are more susceptible to landslides with a level of accuracy greater than 70%. The presented method is, therefore, capable of supporting land planning activities at the regional scale in places where only scarce data are available.</p>

Communality from equal weight for GIS-based landslide susceptibility mapping

2020 ◽  
Author(s):  
Sandip Som ◽  
Saibal Ghosh ◽  
Soumitra Dasgupta ◽  
Thrideep Kumar ◽  
J. N. Hindayar ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Roc Curves ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Equal Weight ◽  
Landslide Susceptibility Index

Abstract Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

scholarly journals Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

2021 ◽  
Author(s):  
Sansar Raj Meena ◽  
Silvia Puliero ◽  
Kushanav Bhuyan ◽  
Mario Floris ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Belief Function ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Veneto Region ◽  
Conditioning Factors ◽  
Topographic Features ◽  
Ne Italy ◽  
Trial And Error Method

Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

scholarly journals Regional landslide hazard assessment through integrating susceptibility index and rainfall process

2020 ◽  
Vol 104 (3) ◽  
pp. 2153-2173
Author(s):  
Zhiheng Wang ◽  
Dongchuan Wang ◽  
Qiaozhen Guo ◽  
Daikun Wang
Keyword(s):  
Environmental Factors ◽  
Hazard Assessment ◽  
Landslide Susceptibility ◽  
Probability Model ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Landslide Hazard Assessment ◽  
Susceptibility Index ◽  
Rainfall Process ◽  
Process Factors

Abstract Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

scholarly journals Regional-Scale Landslide Susceptibility Mapping Using Limited LiDAR-Based Landslide Inventories for Sisak-Moslavina County, Croatia

2021 ◽  
Vol 13 (8) ◽  
pp. 4543
Author(s):  
Iris Bostjančić ◽  
Marina Filipović ◽  
Vlatko Gulam ◽  
Davor Pollak
Keyword(s):  
Predictive Factors ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Spatial Relationship ◽  
Cost Benefit ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Cost Benefit Ratio ◽  
The Cost

In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km2. From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal “Report a landslide” are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors.

scholarly journals Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 483
Author(s):  
Yasin Wahid Rabby ◽  
Yingkui Li
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Safe Zone ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Prediction Rate ◽  
Susceptibility Maps ◽  
Integrated Methods ◽  
Landslide Susceptibility Maps

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

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