Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal)

Abstract. A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal) where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use). Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model) over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i) the definition of thresholds of rainfall (quantity-duration) responsible for past landslide events; (ii) the calculation of the relevant return periods; (iii) the assumption that the same rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area). The landslide hazard is present as the probability of each pixel to be affected by a slope movement, and results from the coupling between the susceptibility map, the prediction-rate curve, and the return periods of critical rainfall events, on a scenario basis. Using this methodology, different hazard scenarios were assessed, corresponding to different rain paths with different return periods.

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LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

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

10.5194/isprs-archives-xlii-4-w12-41-2019 ◽

2019 ◽

Vol XLII-4/W12 ◽

pp. 41-49 ◽

Cited By ~ 1

Author(s):

S. Benchelha ◽

H. Chennaoui Aoudjehane ◽

M. Hakdaoui ◽

R. El Hamdouni ◽

H. Mansouri ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Success Rate ◽

Landslide Susceptibility ◽

Model Building ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Prediction Rate ◽

Artificial Neural

Abstract. The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides. The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI). The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC&thinsp;=&thinsp;0.918) and the prediction rate (AUC&thinsp;=&thinsp;0.901) of the LR model is higher than that of the ANN model (success rate (AUC&thinsp;=&thinsp;0.886) and prediction rate (AUC&thinsp;=&thinsp;0.877). These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.

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Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal

International Journal of Engineering and Management Research ◽

10.31033/ijemr.11.1.23 ◽

2021 ◽

Vol 11 (1) ◽

pp. 167-177

Author(s):

Niraj Baral ◽

Akhilesh Kumar Karna ◽

Suraj Gautam

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Satellite Image ◽

Image Interpretation ◽

Google Earth ◽

Slope Aspect ◽

Susceptibility Map ◽

Conditioning Factors ◽

Prediction Rate ◽

Using Data

Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.

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Landslide susceptibility evaluation based on optimized support vector machine

Proceedings of the ICA ◽

10.5194/ica-proc-4-67-2021 ◽

2021 ◽

Vol 4 ◽

pp. 1-4

Author(s):

Jiping Liu ◽

Rongfu Lin ◽

Shenghua Xu ◽

Yong Wang ◽

Xianghong Che ◽

...

Keyword(s):

Support Vector Machine ◽

Landslide Susceptibility ◽

Firefly Algorithm ◽

Vegetation Index ◽

Shaanxi Province ◽

Slope Aspect ◽

Support Vector ◽

Susceptibility Map ◽

Data Set ◽

Actual Distribution

Abstract. Landslide is a natural disaster that has caused great property losses and human casualties in the world. To strengthen the target prevention and management level, ZhaShui county, Shaanxi province, is selected as the research area to evaluate the landslide susceptibility. First of all, under the premise of considering the correlation, 10 evaluation factors closely related to landslide disaster (i.e., elevation, rainfall, rock group, slope, slope aspect, vegetation index, landform, distance to residential area, distance to road, distance to river system) are taken together with non-landslide points, which are selected under multi-constraint conditions to form a sample data-set. Secondly, the sample dataset is substituted into the Support Vector Machine (SVM) model optimized by firefly algorithm for training and prediction. Finally, the result map was partitioned according to the natural discontinuous point method, and the landslide susceptibility map was obtained. The results show that the model optimized by the firefly algorithm has higher accuracy, and the landslide susceptibility results are more consistent with the actual distribution of disaster points.

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From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-020-01959-x ◽

2021 ◽

Author(s):

Luguang Luo ◽

Luigi Lombardo ◽

Cees van Westen ◽

Xiangjun Pei ◽

Runqiu Huang

Keyword(s):

Ground Motion ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Additive Model ◽

Slope Instability ◽

Susceptibility Map ◽

Jiuzhaigou Earthquake ◽

Full Spectrum ◽

The Past ◽

Time Invariant

AbstractThe vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ($$M_w = 6.5$$ M w = 6.5 ) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

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

Sociedade & Natureza ◽

10.14393/sn-v33-2021-59124 ◽

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.

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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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Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽

10.3390/w11071402 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1402 ◽

Cited By ~ 32

Author(s):

Nohani ◽

Moharrami ◽

Sharafi ◽

Khosravi ◽

Pradhan ◽

...

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Vegetation Index ◽

Slope Angle ◽

Aerial Photographs ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Conditioning Factors ◽

Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

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A New Approach Using AHP to Generate Landslide Susceptibility Maps in the Chen-Yu-Lan Watershed, Taiwan

Sensors ◽

10.3390/s19030505 ◽

2019 ◽

Vol 19 (3) ◽

pp. 505 ◽

Cited By ~ 7

Author(s):

Thi Nguyen ◽

Cheng-Chien Liu

Keyword(s):

Landslide Susceptibility ◽

Binary Classification ◽

Correlation Coefficients ◽

Pairwise Comparison ◽

Drainage Density ◽

Slope Aspect ◽

Bivariate Analysis ◽

Susceptibility Map ◽

Related Factors ◽

New Approach

This paper proposes a new approach of using the analytic hierarchy process (AHP), in which the AHP was combined with bivariate analysis and correlation statistics to evaluate the importance of the pairwise comparison. Instead of summarizing expert experience statistics to establish a scale, we then analyze the correlation between the properties of the related factors with the actual landslide data in the study area. In addition, correlation and dependence statistics are also used to analyze correlation coefficients of preparatory factors. The product of this research is a landslide susceptibility map (LSM) generated by five factors (slope, aspect, drainage density, lithology, and land-use) and pre-event landslides (Typhoon Kalmaegi events), and then validated by post-event landslides and new landslides occurring in during the events (Typhoon Kalmaegi and Typhoon Morakot). Validating the results by the binary classification method showed that the model has reasonable accuracy, such as 81.22% accurate interpretation for post-event landslides (Typhoon Kalmaegi), and 70.71% exact predictions for new landslides occurring during Typhoon Kalmaegi.

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Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

Journal of Nepal Geological Society ◽

10.3126/jngs.v58i0.24601 ◽

2019 ◽

Vol 58 ◽

pp. 163-171 ◽

Cited By ~ 4

Author(s):

Arishma Gadtaula ◽

Subodh Dhakal

Keyword(s):

Landslide Susceptibility ◽

Google Earth ◽

Weight Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Mountainous Area ◽

High Susceptibility ◽

Seismic Landslides ◽

2015 Gorkha Earthquake ◽

Very High

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

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