scholarly journals GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Anna Roccati ◽  
Guido Paliaga ◽  
Fabio Luino ◽  
Francesco Faccini ◽  
Laura Turconi
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Current Model ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk

Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.

scholarly journals Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

2019 ◽  
Vol 8 (12) ◽  
pp. 545 ◽  
Author(s):  
Nayyer Saleem ◽  
Md. Enamul Huq ◽  
Nana Yaw Danquah Twumasi ◽  
Akib Javed ◽  
Asif Sajjad
Keyword(s):  
Risk Assessment ◽  
Landslide Susceptibility ◽  
3D Visualization ◽  
Digital Elevation Models ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Conditioning Factors ◽  
Digital Elevation ◽  
The Impact

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

scholarly journals Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 762 ◽  
Author(s):  
Renwei Li ◽  
Nianqin Wang
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Topographic Wetness Index

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

scholarly journals Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy

2020 ◽  
Author(s):  
Vasil Yordanov ◽  
Maria Antonia Brovelli
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Risk Mitigation ◽  
Model Performance ◽  
Initial Step ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Statistical Index ◽  
Local Conditions ◽  
Basin Scale

Abstract Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations.

scholarly journals Landslide Susceptibility Assessment: Identification and Hazard Mapping of Gandaki Province, Nepal

2020 ◽  
Vol 3 ◽  
pp. 11-21
Author(s):  
Khagendra Raj Poudel ◽  
Ramesh Hamal ◽  
Naresh Paudel
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Entropy Model ◽  
Risk Zone ◽  
Property Loss ◽  
And Coping ◽  
Steep Slopes

 Landslides considered as a common hazard, affecting constantly the administrative territory of Gandaki province, located in the central part of Nepal. Impact of landslides is significant due to its specific geological, anthropic, vegetation and other circumstances. The main aim of this study was to identify the factors determining landslides and forming a landslide susceptibility mapping of study area. The fieldwork was conducted, where 128 GPS locations was recorded throughout the study area. This study also used the maximum entropy model using MaxEnt software, taking into account of various landslide-causing factors, resulting major variables of landslides risk and formed susceptibility mapping of landslide. It is identified that slope and land use land cover are most important variables to increase the landslide risk. Findings highlight that lands around the riversides and steep slopes are more risky area in terms of landslides. Moreover, it is found that the area of 3371.32 km2 measured as landslide risk zone in this province, where Gorkha district categorized as most vulnerable place for landslide, comprising of largest area of landslide risk zone while Parbat district has low amount of risk land. Since the human casualties and property loss are the major consequences of the disaster, it is essential to identify and analyse the factors determining for landslide and developing the landslide susceptibility mapping of Gandaki province, which could be taken into account while developing mitigation and coping strategies.

Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling

Landslides ◽  
2014 ◽  
Vol 12 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Jorge Pedro Galve ◽  
Andrea Cevasco ◽  
Pierluigi Brandolini ◽  
Mauro Soldati
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Risk Mitigation ◽  
Shallow Landslide ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Probabilistic Modelling

scholarly journals Landslide Susceptibility Mapping and Assessment of Ketti Micro Watershed in Nilgiris District using Remote Sensing and Gis Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 142-148
Author(s):  
P. Kodanda Rama Rao ◽  
C. Rajakumar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Field Study ◽  
Landslide Susceptibility ◽  
Remote Sensing And Gis ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Thematic Maps ◽  
Soil Thickness ◽  
Gis And Remote Sensing

The GIS and Remote sensing technologies have been useful in the field of mapping in recent days. It is possible to integrate spatial data’s of different layers to determine the influence of various factors on landslide incidences. Based on the parameters such as slope, geomorphology, lineament, aspect, and present land use and soil thickness various thematic maps were prepared. By assessing proper ranks and weights the final landslide susceptible map was prepared. These maps were validated during field study

scholarly journals Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

2013 ◽  
Vol 1 (5) ◽  
pp. 5199-5236
Author(s):  
P. I. M. Camarinha ◽  
V. Canavesi ◽  
R. C. S. Alvalá
Keyword(s):  
Risk Assessment ◽  
Spatial Data ◽  
Urban Areas ◽  
Coastal Region ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Serra Do Mar

Abstract. In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil – Santos, Cubatão, Caraguatatuba and Ubatuba – in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

scholarly journals Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Alembante Genene ◽  
Matebie Meten
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Image Interpretation ◽  
Active Surface ◽  
Google Earth ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect

Abstract The study area is found in Gindeberet district of West Shewa zone in Oromia Regional State of Ethiopia.This area is highly susceptible to active surface processes due to the presence of rugged morphology with steep scarps, sharp ridges, cliffs, deep gorges and valleys. This study aimed to identify and evaluate the causative factors and to prepare the landslide susceptibility maps (LSMs) of the study area. Two bivariate statistical models i.e. Information value(IV) and the Frequency ratio(FR), were used. First, active, reactivated and passive landslides and scarps were identified using Google Earth image interpretation and extensive field survey for landslide inventory. A total of 580 landslide were randomly selected into two datasets in which (80%)460 landslides were used for modeling and (20%)116 landslidesfor validation. conditioning factors (slope, aspect, curvature, distance from stream, distance from lineaments, lithology, rainfall and land use) were combined with a training landslide dataset in a ArcGIS to generate LSMs which weredivided into verylow, low, moderate, high and veryhigh susceptibility zones. LSMs for IV and FR models were validated using the Area under(ROC) curve showing a success rate of 0.836 and 0.835 respectively and a predictive rate of 0.817 and 0.818 respectively wich showed a good performance of both models. The resulting LSMs can be used for land use planning and management.

scholarly journals AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

2019 ◽  
Vol IV-2/W5 ◽  
pp. 503-511 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
M. B. A. Gibril ◽  
U. S. Lay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Inventory Data ◽  
Prediction Rate

<p><strong>Abstract.</strong> Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.</p>

LANDSLIDE-SUSCEPTIBILITY MAPPING AND RISK ASSESSMENT, EASTERN KENTUCKY

2020 ◽  
Author(s):  
Matthew Crawford ◽  
◽  
Hudson Koch ◽  
Jason Dortch ◽  
Ashton A. Killen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Eastern Kentucky
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