scholarly journals Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island

Geosciences ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 350 ◽  
Author(s):  
Christos Polykretis ◽  
Kleomenis Kalogeropoulos ◽  
Panagiotis Andreopoulos ◽  
Antigoni Faka ◽  
Andreas Tsatsaris ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Slope Angle ◽  
Slope Aspect ◽  
Ground Acceleration ◽  
Bivariate Model ◽  
Susceptibility Maps ◽  
Emergency Actions ◽  
Data Layers ◽  
Lefkada Island ◽  
Analysis Models

The main purpose of this study is to comparatively assess the susceptibility of earthquake-triggered landslides in the island of Lefkada (Ionian Islands, Greece) using two different statistical analysis models, a bivariate model represented by frequency ratio (FR), and a multivariate model represented by logistic regression (LR). For the implementation of the models, the relationship between geo-environmental factors contributing to landslides and documented events related to the 17th November 2015 earthquake was investigated by geographic information systems (GIS)-based analysis. A landslide inventory with events attributed to the specific earthquake was prepared using satellite imagery interpretation and field surveys. Eight factors: Elevation, slope angle, slope aspect, distance to main road network, distance to faults, land cover, geology, and peak ground acceleration (PGA), were considered and used as thematic data layers. The prediction capability of the models and the accuracy of the resulting susceptibility maps were tested by a standard validation method, the receiver operator characteristic (ROC) analysis. Based on the validation results, the output map with the highest reliability could potentially constitute an ideal basis for use within regional spatial planning as well as for the organization of emergency actions by local authorities.

scholarly journals Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

2020 ◽  
Vol 9 (12) ◽  
pp. 696
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Xia Zhao ◽  
Xinxiang Lei ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Spatial Prediction ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Bivariate Model ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

scholarly journals Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

2021 ◽  
Author(s):  
Anna Małka
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Mass Movement ◽  
Slope Angle ◽  
Geographic Information ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Selection Of

AbstractThis work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

scholarly journals A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 695 ◽  
Author(s):  
Qiang Dou ◽  
Shengwu Qin ◽  
Yichen Zhang ◽  
Zhongjun Ma ◽  
Junjun Chen ◽  
...  
Keyword(s):  
Debris Flow ◽  
Information Fusion ◽  
Slope Angle ◽  
Controlling Factors ◽  
Susceptibility Mapping ◽  
Jilin Province ◽  
Vegetation Coverage ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Debris Flow Susceptibility

Debris flow is one of the most frequently occurring geological disasters in Jilin province, China, and such disasters often result in the loss of human life and property. The objective of this study is to propose and verify an information fusion (IF) method in order to improve the factors controlling debris flow as well as the accuracy of the debris flow susceptibility map. Nine layers of factors controlling debris flow (i.e., topography, elevation, annual precipitation, distance to water system, slope angle, slope aspect, population density, lithology and vegetation coverage) were taken as the predictors. The controlling factors were improved by using the IF method. Based on the original controlling factors and the improved controlling factors, debris flow susceptibility maps were developed while using the statistical index (SI) model, the analytic hierarchy process (AHP) model, the random forest (RF) model, and their four integrated models. The results were compared using receiver operating characteristic (ROC) curve, and the spatial consistency of the debris flow susceptibility maps was analyzed while using Spearman’s rank correlation coefficients. The results show that the IF method that was used to improve the controlling factors can effectively enhance the performance of the debris flow susceptibility maps, with the IF-SI-RF model exhibiting the best performance in terms of debris flow susceptibility mapping.

Liquefaction susceptibility maps for the Aqaba–Elat region with projections of future hazards with sea-level rise

2020 ◽  
pp. qjegh2020-039 ◽  
Author(s):  
Abdel-Rahman A. Abueladas ◽  
Tina M. Niemi ◽  
Abdallah Al-Zoubi ◽  
Gideon Tibor ◽  
Mor Kanari ◽  
...  
Keyword(s):  
Sea Level ◽  
Water Table ◽  
Active Faults ◽  
Gulf Of Aqaba ◽  
Susceptibility Map ◽  
Northern Coast ◽  
Borehole Data ◽  
Ground Acceleration ◽  
Liquefaction Susceptibility ◽  
Susceptibility Maps

The cities of Aqaba, Jordan and Elat, Israel are vulnerable to seismic damage because they are built over the active faults of the Dead Sea Transform that are the source of historically destructive earthquakes. A liquefaction susceptibility map was generated for the Aqaba–Elat region. Borehole data from 149 locations and the water table depth were used to calculate effective overburden stress in the Seed–Idriss simplified method. The liquefaction analysis was based on applying a cyclic loading scenario with horizontal peak ground acceleration of 0.3 g in a major earthquake. The liquefaction map, compiled using a GIS platform, shows high and moderate liquefaction susceptibility zones along the northern coast of the Gulf of Aqaba that extend 800 m inland from the shoreline. In Aqaba, several hotels, luxury apartment complexes, archaeological sites, ports and commercial districts are located within high and moderate liquefaction zones. In Elat, the seaport and the coastal hotel district are located within a high susceptibility zone. Most residential areas, schools and hospitals in both cities are located within zones not susceptible to liquefaction based on the methods of this study. The total area with the potential to be liquefied along the Gulf of Aqaba is c. 10 km2. Given predictions for global sea-level, we ran three liquefaction models utilizing projected water table rises of 0.5, 1 and 2 m. These models yielded an increase in the area of high liquefaction ranging from 26 to 49%. Given the high potential of future earthquakes, our liquefaction susceptibility maps should help inform city officials for hazard mitigation planning.

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals How Can Geomorphology Inform Ecological Restoration? A Synthesis of Geophysical and Biological Assessment to Determine Restoration Priorities

2021 ◽  
Author(s):  
◽  
Leicester Cooper
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Reference Site ◽  
Slope Angle ◽  
Slope Aspect ◽  
Total N ◽  
Olsen P ◽  
Restoration Site ◽  
Soil Variables ◽  
Indigenous Forest

<p>The central concern that this study addresses is how an understanding of geomorphological processes and forms may inform ecological restoration; particularly practical restoration prioritisation. The setting is that of a hill country gully system covered in grazing pasture which historically would have been cloaked in indigenous forest. The study examines theory in conjunction with an application using a case study centred on Whareroa Farm (the restoration site) and Paraparaumu Scenic Reserve (the reference site) on the southern Kapiti Coast, north of Wellington. The impact that the change of land use has had on the soil and geomorphic condition of Whareroa and the influence the changes may have on the sites restoration is investigated. The thesis demonstrates a method of choosing reference sites to be used as templates for rehabilitating the restoration site. Geographical Information Systems and national databases are used and supplemented with site inspection. The reference site chosen, Paraparaumu Scenic Reserve, proved to be a good template for the restoration site particularly given that it is located in the midst of a heavily modified area. On-site inspection considering dendritic pattern and floristic composition confirms the database analysis results. Soil variables (bulk density, porosity, soil texture, pH, Olsen P, Anaerobic Mineralisable N, Total N (AMN), Total C and C:N ratio) are investigated and statistical comparisons made between the sites to quantify changes due to land-use change, i.e. deforestation and subsequent pastoral grazing. Factors investigated that may explain the variation in the soil variables were site (land use), hillslope location, slope aspect, and slope angle. Permutation tests were conducted to investigate the relationships between the independent factors and the SQI (dependent soil variables). Land use and slope angle were most frequent significant explanatory factors of variation, followed by hillslope location whilst slope aspect only influenced soil texture. A number of soil variables at Whareroa were found to be outside the expected range of values for an indigenous forest soil including AMN, Total N, Olsen P, and pH ...</p>

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

Landslide susceptibility mapping using GIS and Remote Sensing: A case study of the Rangamati District, Bangladesh

2020 ◽  
Author(s):  
Afruja Begum ◽  
Md Shofiqul Islam ◽  
Md. Muyeed Hasan
Keyword(s):  
Remote Sensing ◽  
Water Pressure ◽  
Research Work ◽  
Slope Angle ◽  
Steep Slope ◽  
Landslide Susceptibility Mapping ◽  
Dominant Factor ◽  
Natural Phenomenon ◽  
Slope Aspect ◽  
Gis And Remote Sensing

Abstract The landslide is a natural phenomenon and one of the most commonplace disasters in the Rangamati Hill tract area which appeals for better forecasting and specify the landslide susceptible zonation. This research work examines the application of GIS and Remote Sensing techniques based on different parameters such as altitude, slope angle, slope aspect, rainfall, land-use land-cover (LULC), geology and stream distance by heuristic model to identify the landslide susceptible zones for the study area. Among the parameters, rainfall, steep slope, geology and LULC are the dominant factor that triggering the landslide. Clayey or silty soils of the study area during heavy and prolong rainfall behave a flow of debris due to water pressure within the soil, resulting landslides. Steep slope has greater influences for weather zones of the rock-masses for susceptible landslides. Result and field observation indicate that the population density and LULC has a vital effect on landslide within the study area. However, landslide susceptible zones were created based on the susceptibility map of the study area which shows that about 19.43% of the area are at low susceptible zone, 56.55% of the area are at medium susceptible zone, 19.19% of the area are in the high susceptible zone and 4.81% of the area is at the very high susceptible zone.

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