Regional Scale Landslide Susceptibility Mapping in Emilia Romagna (Italy) as a Tool for Early Warning

2014 ◽  
pp. 443-449 ◽  
Author(s):  
Daniela Lagomarsino ◽  
Samuele Segoni ◽  
Riccardo Fanti ◽  
Filippo Catani ◽  
Nicola Casagli
Keyword(s):  
Early Warning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

TXT-tool 1.084-3.1: Landslide Susceptibility Mapping at a Regional Scale in Vietnam

2017 ◽  
pp. 161-174
Author(s):  
Quoc Hung Le ◽  
Thi Hai Van Nguyen ◽  
Minh Duc Do ◽  
Thi Chau Ha Le ◽  
Van Son Pham ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

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.

scholarly journals PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan

2021 ◽  
Vol 13 (20) ◽  
pp. 4129
Author(s):  
Muhammad Afaq Hussain ◽  
Zhanlong Chen ◽  
Run Wang ◽  
Muhammad Shoaib
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Susceptibility Mapping ◽  
Data Availability ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Gradient Boosting ◽  
Persistent Scatterer Interferometry ◽  
Extreme Gradient Boosting ◽  
Deformation Velocity

Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.

scholarly journals GIS-based earthquake-triggered landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

2019 ◽  
Author(s):  
Yaning Yi ◽  
Zhijie Zhang ◽  
Wanchang Zhang ◽  
Qi Xu ◽  
Cai Deng ◽  
...  
Keyword(s):  
Analytical Hierarchy Process ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Sichuan Province ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Index Model ◽  
Weighted Index ◽  
Hierarchy Process

Abstract. A Ms 7.0 earthquake struck the Jiuzhaigou region of Sichuan Province, China at 21:19 pm on Tuesday, 8 August 2017, which triggered a large number of landslides. For mitigating the damages of earthquake-triggered landslides to individuals and infrastructures of the earthquake affected region, a comprehensive landslide susceptibility mapping was attempted with an integrated weighted index model by combining the frequency ratio and the analytical hierarchy process approaches under GIS-based environment in the earthquake heavily attacked Zhangzha town of the Jiuzhaigou region. For this purpose, a total number of 842 earthquake-triggered landslides were visually interpreted and located from Sentinel-2A images acquired before and after the earthquake at first, and then the recognized landslides were randomly split into two groups to establish the earthquake-triggered landslide inventory, among which 80 % of the landslides was used for training the integrated model and the remaining 20 % for validation. Nine landslide controlling factors, namely slope, aspect, elevation, lithology, distance from faults, distance from rivers, land-use/cover, normalized difference vegetation index and peak ground acceleration, were considered with an integrated weighted index model for determination of the weighted index through analysing their relationships with occurrence frequency ratios of landslides with analytical hierarchy process approaches. Furthermore, an area under the curve approach was adopted to comprehensively evaluate the performance of the integrated weighted index model, including the degree of model fit and model predictive capability. The results demonstrated the reliability and feasibility of the integrated weighted index model in earthquake-triggered landslide susceptibility mapping at regional scale. The generated map can be served as the scientific basis to mitigate hazards of the earthquake-triggered landslides to individuals and infrastructures of the earthquake affected region.

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