Distribution of landslides reconstructed from inventory data and estimation of landslide susceptibility in Hungary

The complexity of landslides makes it difficult to predict the spatial distribution of landslide susceptibility and hazard. Although in most European countries the basic preconditions for the occurrence of mass movements (rocks and topography) have been mapped in detail, the triggering factors (e.g. precipitation or earthquakes) are much less predictable. A detailed nation-wide inventory for Hungary provides a unique base for landslide susceptibility mapping. As the methodology for the assessment the technique applied in the ELSUS 1000 project was selected. The micro-regions of Hungary were identified where mass movements contribute to land degradation. The paper provides a statistical evaluation of the distribution of landslides, depicts landslide susceptibility on maps and reveals the role of anthropogenic factors in the generation of mass movements. The mid-resolution elevation model (SRTM1), land cover data (CLC50) and surface geology database (Mining and Geological Survey of Hungary) allowed for the derivation of a landslide susceptibility map more detailed than before. Along with its background information the map reflects and explains the differences in landslide susceptibility among the individual hilly and mountainous regions.

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Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas

Geosciences ◽

10.3390/geosciences9080360 ◽

2019 ◽

Vol 9 (8) ◽

pp. 360 ◽

Cited By ~ 2

Author(s):

Sansar Raj ◽

Thimmaiah

Keyword(s):

Digital Elevation Model ◽

Spatial Resolution ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Mountainous Regions ◽

Digital Elevation ◽

Kullu Valley ◽

Elevation Model ◽

The Impact

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

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GIS-based Landslide Susceptibility Mapping Using Logistic Regression Analysis: A Case study in the Kadernath valley, Central Himalaya.

10.5194/egusphere-egu2020-8042 ◽

2020 ◽

Author(s):

Suman Das

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Landslide Susceptibility ◽

Sensor Data ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Anthropogenic Factors ◽

Susceptibility Map

<p>Himalayan Terrain is highly susceptible to landslide events triggered by frequent earthquakes and heavy rainfall. In the recent past, cloud burst events are on rising, causing massive loss of life and property, mainly attributed to climate change and extensive anthropogenic activities in the mountain region as experienced in case of 2013 Kedarnath Tragedy. The study aimed to identify the potential landslide hazard zone in Mandakini valley by utilizing different types of data including Survey of India toposheet, geological (lithological and structural) maps, IRS-1D, LISS IV multispectral and PAN satellite sensor data and field observations. Relevant 18 thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, LULC, NDVI, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. &#160;A detailed landslide susceptibility map was produced using a logistic regression method with datasets developed in GIS. which has further been categorized into four landslide susceptibility zones from high to very low. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. ROC curve analysis showing an accuracy of 87.3 % for an independent set of test samples. The result also showed a strong agreement between the distribution of existing landslides and predicted landslide susceptibility zones. Consequently, this study could serve as an effective guide for further land-use planning and for the implementation of development.</p>

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Artificial intelligence approaches for spatial prediction of landslides in mountainous regions of western India

10.21203/rs.3.rs-251440/v1 ◽

2021 ◽

Author(s):

PANKAJ PRASAD ◽

Victor Joseph Loveson ◽

Sumit Das ◽

Priyankar Chandra

Keyword(s):

Artificial Intelligence ◽

Landslide Susceptibility ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

Susceptibility Map ◽

Western India ◽

Boosted Regression Tree ◽

Study Region ◽

Mountainous Regions ◽

Stochastic Gradient Boosting

Abstract The prediction of landslide is a complex task but preparing the landslide susceptibility map through artificial intelligence approaches can reduce life loss and damages resulting from landslides. The purpose of this study is to evaluate and compare the landslide susceptibility mapping (LSM) using six machine learning models, including random forest (RF), deep boost (DB), stochastic gradient boosting (SGB), rotation forest (RoF), boosted regression tree (BRT), and logit boost (LB) in the mountainous regions of western India. The landslide inventory map consisting of 184 landslide locations has been divided into two groups for training (70% dataset) and validation (30% dataset) purposes. Fourteen landslide triggering factors including slope, topographical roughness index, road density, topographical wetness index, elevation, slope length, drainage density, stream power index, geomorphology, rainfall, soil, lithology, lineament density, and normalized difference vegetation index have been considered using the boruta approach for the LSM. The results reveal that the RF model has the highest precision in terms of AUC (0.88; 0.89), kappa (0.62; 0.50), accuracy (0.81; 0.77), and specificity (0.86; 0.86) both in the study region and secondary region, respectively. Hence, it can be concluded that the RF is an effective and promising technique as compared to DB, SGB, RoF, BRT, and LB for landslide susceptibility assessment in the research area as well as in regions having similar geo-environmental configuration.

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Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

Journal of Disaster Research ◽

10.20965/jdr.2021.p0529 ◽

2021 ◽

Vol 16 (4) ◽

pp. 529-538

Author(s):

Thi Thanh Thuy Le ◽

The Viet Tran ◽

Viet Hung Hoang ◽

Van Truong Bui ◽

Thi Kien Trinh Bui ◽

...

Keyword(s):

Landslide Susceptibility ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

High Susceptibility ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Mountainous Regions ◽

Geological Conditions ◽

Very High

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.

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LANDSLIDE SUSCEPTIBILITY MAPPING OF THE NORTHEASTERN PART OF ACHAIA PREFECTUREUSING ANALYTICAL HIERARCHICAL PROCESS AND GIS TECHNIQUES

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.11338 ◽

2017 ◽

Vol 43 (3) ◽

pp. 1637

Author(s):

D. Rozos ◽

D.G. Bathrellos ◽

D.H. Skilodimou

Keyword(s):

Land Use Planning ◽

Landslide Susceptibility ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Analytical Hierarchical Process ◽

Stability Of Slopes ◽

High Zone ◽

Integrated Technique ◽

The Individual

Landslides are one of the most frequent and disastrous natural hazards worldwide. Thus, the need of landslide susceptibility maps is of primary importance as they are both a useful tool for the land use planning and a necessary step for future development activities. This paper presents an integrated technique of analytical hierarchical process (AHP) and geographic information system (GIS) to create a landslide susceptibility map of the NE part of Achaia prefecture. The study area mainly consists of Neogene deposits and it is a part of the Corinthian graben, which characterized by intense neotectonic activity. Therefore, it is affected by many slope movements that usually cause serious damages in inhabitant areas and road networks. Based on field survey data analysis six parameters were chosen as major parameters that influence the stability of slopes to the direction of landslide manifestation. The AHP method identifies both the rate of the individual classes, and the weight of each factor. Spatial layers with their corresponding rates and weights were linearly combined to prepare the landslide susceptibility map, which includes four zones of slope movement’s susceptibility, namely a low, a moderate a high and a very high zone. The evaluation and final confirmation of the map was based on a great number of recorded landslides in the area.

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Weights of evidence method for landslide susceptibility mapping in Tangier, Morocco

MATEC Web of Conferences ◽

10.1051/matecconf/201814902042 ◽

2018 ◽

Vol 149 ◽

pp. 02042

Author(s):

Mahfoud Bousta ◽

Lahsen Ait Brahim

Keyword(s):

Landslide Susceptibility ◽

Forest Cover ◽

Agricultural Land ◽

Weight Of Evidence ◽

Landslide Susceptibility Mapping ◽

Economic Losses ◽

Susceptibility Map ◽

Mass Movements ◽

Ground Truth Data ◽

Adopted Model

Tangier region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Tangier using the Weight of Evidence method (WofE). The method is founded on the principle that an event (landslide) is more likely to occur based on the relationship between the presence or absence of a predictive variable (predisposing factors) and the occurrence of this event. The inventory, description and analysis of mass movements were prepared. Then the main factors governing their occurrence (lithology, fault, slope, elevation, exposure, drainage and land use) were mapped before applying WofE. Finally, the ROC curves were established and the areas under curves (AUC) were calculated to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. The prediction accuracy was found to be 70%. Obtained susceptibility map shows that 60% of inventoried landslides are in the high to very high susceptibility zones, which is very satisfactory for the validation of the adopted model and the obtained results. These zones are mainly located in the N-E and E part of the Tangier region in the soft and fragile facies of the marls and clays of the Tangier unit, where landuse is characterized by dominance of arable and agricultural land (lack of forest cover). From a purely spatial point of view, the localization of these two classes of susceptibility is completely corresponding to the ground truth data, that is to say that all the environmental and anthropogenic conditions are in place for making this area prone to landslide hazards. The obtained map is a decision-making tool for presenting, comparing and discussing development and urban scenarios in Tangier. These results fall within the context of sustainable development and will help to mitigate the socio-economic impacts usually observed when landslides are triggered.

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Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

Remote Sensing ◽

10.3390/rs13020238 ◽

2021 ◽

Vol 13 (2) ◽

pp. 238

Author(s):

Zhice Fang ◽

Yi Wang ◽

Gonghao Duan ◽

Ling Peng

Keyword(s):

Decision Trees ◽

Landslide Susceptibility ◽

Ensemble Methods ◽

Landslide Susceptibility Mapping ◽

Three Gorges Reservoir Area ◽

Ratio Method ◽

Susceptibility Map ◽

Rotation Forest ◽

Predictive Values ◽

Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

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A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020093 ◽

2021 ◽

Vol 10 (2) ◽

pp. 93

Author(s):

Wei Xie ◽

Xiaoshuang Li ◽

Wenbin Jian ◽

Yang Yang ◽

Hongwei Liu ◽

...

Keyword(s):

Machine Learning ◽

Hybrid Method ◽

Roc Curve ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Assessment Model ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Susceptibility Map ◽

Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

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Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs13112166 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2166

Author(s):

Xin Yang ◽

Rui Liu ◽

Mei Yang ◽

Jingjue Chen ◽

Tianqiang Liu ◽

...

Keyword(s):

Land Use ◽

Hybrid Model ◽

Landslide Susceptibility ◽

Spatial Information ◽

Landslide Susceptibility Mapping ◽

Ratio Method ◽

Support Vector ◽

Susceptibility Map ◽

Conditioning Factors ◽

Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

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LANDSLIDE SUSCEPTIBILITY ASSESSMENT THROUGH FUZZY LOGIC INFERENCE SYSTEM (FLIS)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w1-355-2016 ◽

2016 ◽

Vol XLII-4/W1 ◽

pp. 355-360 ◽

Cited By ~ 2

Author(s):

T. Bibi ◽

Y. Gul ◽

A. Abdul Rahman ◽

M. Riaz

Keyword(s):

Fuzzy Logic ◽

Spatial Data ◽

Landslide Susceptibility ◽

Landslide Susceptibility Mapping ◽

Hazard Mapping ◽

Complex Nature ◽

Susceptibility Map ◽

Likelihood Ratios ◽

Regular Feature ◽

Inference System

Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

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