Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.

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Development of Rainfall threshold model for the watershed/sub-watershed landscape at data scarce situation – a case study of mid-hill region, Nepal.

10.5194/egusphere-egu21-4797 ◽

2021 ◽

Author(s):

Sanjaya Devkota ◽

Deepak Kc ◽

Michel Jaboyedoff ◽

Govind Acharya

Keyword(s):

Threshold Model ◽

Monsoon Season ◽

Warning System ◽

Daily Rainfall ◽

Rainfall Threshold ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Susceptibility Map ◽

Hill Region ◽

Threshold Exceedance

Landslides are common in the mid-hill region of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal, the casual and triggering factors of the landslides are respectively the underlying geology, intense rainfall and unplanned construction of rural roads is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides at the watershed landscape is data driven, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available historical landslides date has been used to develop the rainfall threshold model for the two watersheds in central and western mid-hill regions respectively the Sindhukhola and Sotkhola in Bagmati and Karnali Provinces of Nepal. The watersheds are located in two distinct hydro-climatic regions in terms of rainfall amount and intensity. Historical daily (monsoonal) rainfall data of over four decades (1970-2016) were analyzed available from the Department of Hydrology and Meteorology (DHM)/Government of Nepal and five days&#8217; antecedent rain was calculated. With the limitedly available temporal landslides data, correlation was examined among the 5-days antecedent rain (mm/5days) and daily rainfall (mm/day) portrayed the rainfall threshold (RT) model (Sindhukhola=180-1.07RT5adt and Sotkhola = 110-0.83*RT5adt). Utilizing the five days&#8217; antecedent rain fitted into the model, results the threshold rainfall. Deducting the five days&#8217; antecedent rains to the RT described the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in Y-axis and threshold exceedance R in X-axis) to visualize the changes in the threshold exceedance over time, whenever the threshold exceedance progressively and rapidly increased and crossed the threshold line and reached to the positive (> 0) zone, the plots allows for the landslides warning notice. In case of the threshold exceedance is further increased there is likely to have landslides in the watersheds. The model was validated with the 35 dated landslides recorded in monsoon 2015 in Sotkhola watershed. The result indicated that the model preserves 72% coefficient of determination (R2) where there were landslides in the watershed during 2015 monsoon. Due to the simplicity and at the data scarce situation, the model was found to be useful to forecast the landslides during the monsoon season in the region. The model; however, can be improved for better performance whenever the higher resolution time-series landslides data and automated weather stations are available in the watersheds. Linking this model to the proper landslide susceptibility map, and the real time rainfall data through mobile communication techniques, landslide early warning system can be established.KEYWORDS: landslide, rainfall threshold, data-scare, antecedent rainfallReferences:Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73(3-4), 247-265.Jaiswal, P. and van Westen, C.J., 2009. Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2): 96-105.Acknowledgement: Comprehensive Disaster Risk Management Programme &#8211; UNDP in Nepal for the opportunity to conduct this research.

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Using GIS & Multicriteria Decision analysis in landslide susceptibility mapping - a case study in Messinia prefecture area (SW Peloponnesus, Greece)

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.17240 ◽

2007 ◽

Vol 40 (4) ◽

pp. 1973 ◽

Cited By ~ 13

Author(s):

I. Ladas ◽

I. Fountoulis ◽

I. Mariolakos

Keyword(s):

Decision Analysis ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Construction Projects ◽

Multicriteria Decision Analysis ◽

Combination Method ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Analytic Hierarchy ◽

Multicriteria Decision

The purpose of this study is to assess the susceptibility of landslides at the eastern part of Messinia prefecture using GIS and Multicriteria Decision Analysis. Analytic Hierarchy Process and Weighted Linear Combination method were used to create a landslide susceptibility map for the study area. The produced map provides valuable information concerning the stability conditions of the territory and may serve as the first step in a complete hazard assessment towards the mitigation of natural landslide disasters in Messinia Prefecture area. Particularly the intention is to transfer effectively information regarding slope stability to non-geologists who take decisions for future land use planning processes and major construction projects.

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Combining landslide susceptibility maps and rainfall thresholds using a matrix approach

Revista de Geomorfologie ◽

10.21094/rg.2017.003 ◽

2017 ◽

Vol 19 (1) ◽

pp. 58-74

Author(s):

THIEBES Benni ◽

BAI Shibiao ◽

XI Yanan ◽

GLADE Thomas ◽

BELL Rainer

Keyword(s):

Landslide Susceptibility ◽

Regional Scale ◽

Daily Rainfall ◽

Rainfall Threshold ◽

Landslide Susceptibility Mapping ◽

Matrix Approach ◽

Hazard Maps ◽

Rainfall Thresholds ◽

Rainfall Model ◽

Simple Matrix

On the regional scale, investigations on future landslide can broadly be distinguished in spatial or temporal analyses, i.e. landslide susceptibility or hazard maps, and landslide triggering rainfall thresholds. Even though both approaches have its uses e.g. in spatial planning, risk management and early warning, they also have limitations. Susceptibility and hazard maps do not contain information on when landslides will be triggered, while rainfall thresholds give no detailed indication on where a landslide might take place. The combination of spatial and temporal landslide research remains a complex issue and no ready-to-use methodology for combined spatiotemporal landslide analyses is presently available. In our study, we present a simple matrix approach to combine spatial and temporal landslide probabilities and highlight its application for a case study in the Wudu region, China. Landslide susceptibility mapping is based on a previous study involving logistic regression; the analysis of rainfall threshold was carried out applying the daily rainfall model. A 4x4 matrix was used to combine and reclassify the spatial and temporal landslide information. The results are then plotted on a map to highlight the susceptibility for rainfall events with varying likelihood of triggering landslides.

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Landslide susceptibility mapping of SE Serbia using GIS

Geoloski anali Balkanskog poluostrva ◽

10.2298/gabp1902105d ◽

2019 ◽

Vol 80 (2) ◽

pp. 105-116

Author(s):

Sonja Djokanovic

Keyword(s):

Landslide Susceptibility ◽

Slope Angle ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Susceptibility Assessment ◽

High Susceptibility ◽

Landslide Susceptibility Map ◽

Analytic Hierarchy ◽

Hierarchy Process ◽

Very High

Landslides represent a great problem in Serbia. According to current estimates 30-35 % of Serbia is affected by landslides. In this paper a landslide susceptibility analysis is done for SE Serbia. Study area covers 1507 km2. Relief is hilly or mountainous and characterized by high altitude differences. Analysis is done by geographic information system (GIS) and evaluation by analytic hierarchy process (AHP). For susceptibility assessment are used four factors: lithology, slope angle, distance to rivers and distance to faults. The most landslides are formed on slope steepness less than 30?. There is four classes of susceptibility in study area. Zone of very high susceptibility make 63.9 % of the study area. Zone of high susceptibility covers 15.7 % of the study area. The moderate class occupies 37.4% and zone classified as having low susceptibility accounts for 10 % of study area. Final landslide susceptibility map of SE Serbia is satisfactory.

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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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Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

Journal of Geology Geography and Geoecology ◽

10.15421/112163 ◽

2021 ◽

Vol 30 (4) ◽

pp. 683-691

Author(s):

G. Kavitha ◽

S. Anbazhagan ◽

S. Mani

Keyword(s):

Landslide Susceptibility ◽

Drainage Density ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Landslide Susceptibility Zonation ◽

Road Section ◽

Gps Measurement ◽

Causative Factors ◽

Very High

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

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Production of Landslide Susceptibility Map using Bayesian Probability Model

International Journal of 3-D Information Modeling ◽

10.4018/ij3dim.2015040102 ◽

2015 ◽

Vol 4 (2) ◽

pp. 16-33 ◽

Cited By ~ 1

Author(s):

Halil Akıncı ◽

Ayşe Yavuz Özalp ◽

Mehmet Özalp ◽

Sebahat Temuçin Kılıçer ◽

Cem Kılıçoğlu ◽

...

Keyword(s):

Land Use ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Probability Model ◽

Bayesian Probability ◽

Slope Aspect ◽

Susceptibility Map ◽

Model Parameters ◽

Operating Characteristics ◽

Landslide Susceptibility Map

Artvin is one of the provinces in Turkey where landslides occur most frequently. There have been numerous landslides characterized as natural disaster recorded across the province. The areas sensitive to landslides across the province should be identified in order to ensure people's safety, to take the necessary measures for reducing any devastating effects of landslides and to make the right decisions in respect to land use planning. In this study, the landslide susceptibility map of the Central district of Artvin was produced by using Bayesian probability model. Parameters including lithology, altitude, slope, aspect, plan and profile curvatures, soil depth, topographic wetness index, land cover, and proximity to the road and stream were used in landslide susceptibility analysis. The landslide susceptibility map produced in this study was validated using the receiver operating characteristics (ROC) based on area under curve (AUC) analysis. In addition, control landslide locations were used to validate the results of the landslide susceptibility map and the validation analysis resulted in 94.30% accuracy, a reliable outcome for this map that can be useful for general land use planning in Artvin.

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