scholarly journals Landslide Susceptibility in Majalengka Regency, West Java Province

2021 ◽  
Vol 884 (1) ◽  
pp. 012053
Author(s):  
S Selaby ◽  
E Kusratmoko ◽  
A Rustanto
Keyword(s):  
Landslide Susceptibility ◽  
Public Works ◽  
Disaster Mitigation ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Multi Criteria Evaluation ◽  
Susceptibility Maps ◽  
The Government ◽  
The Impact ◽  
Landslide Susceptibility Maps

Abstract Majalengka is one of districts in Indonesia which is susceptible to landslides. Landslides in Majalengka caused enormous losses such as damage to infrastructure, loss of property, and even human fatalities. Seeing of the impact, mitigation efforts are needed to reduce risks and losses by making landslide susceptibility maps. This study aims to map areas landslide susceptibility and as a reference for the government and related agencies to reduce losses. The method used overlay using Spatial Multi-Criteria Evaluation (SMCE), using weighting values from the Minister Public Works Regulation NO.22/PRT/M/2007, Puslittanak Bogor (2014) and Directorate Volcanology and Disaster Mitigation (DVMBG) (2004). Then comparison of these sources is carried out to determine weighting value with the highest accuracy. The variables are slope, rainfall, soil type, lithology, and land use. The results of this study indicate that landslide susceptibility areas are divided into non-susceptible, low, moderate, and high areas. Where areas Majalengka Regency is dominated by moderate susceptibility level. For the accuracy value of the landslide susceptibility map produced by the weighted value source from the Minister of Public Works Regulation NO.22/PRT/M/2007 has the highest accuracy value of 76%. For weighting from the Bogor Puslittanak is 73%, while weighting source from DVMBG is 68%.

scholarly journals LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1229-1236 ◽  
Author(s):  
G. Karakas ◽  
R. Can ◽  
S. Kocaman ◽  
H. A. Nefeslioglu ◽  
C. Gokceoglu
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Digital Terrain Model ◽  
Disaster Mitigation ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Effective Parameters ◽  
Landslide Susceptibility Map ◽  
Random Forest Method ◽  
Susceptibility Maps

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

scholarly journals Retrospective evaluation of landslide susceptibility maps and review of validation practice

2021 ◽  
Vol 80 (15) ◽  
Author(s):  
Paul Fleuchaus ◽  
Philipp Blum ◽  
Martina Wilde ◽  
Birgit Terhorst ◽  
Christoph Butscher
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Susceptibility Map ◽  
Retrospective Evaluation ◽  
Widespread Application ◽  
Evaluation Approach ◽  
Susceptibility Maps ◽  
Landslide Event ◽  
Landslide Susceptibility Maps

AbstractDespite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future.

GIS-Based Logistic Regression for Landslide Susceptibility Analysis in Western Washington State

2017 ◽  
Vol 8 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Lucas A. Dailey ◽  
Sven Fuhrmann
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Puget Sound ◽  
Washington State ◽  
Susceptibility Map ◽  
The North ◽  
Susceptibility Maps ◽  
Western Washington ◽  
Landslide Susceptibility Maps ◽  
Puget Sound Region

The Oso landslide, one of the most recent disasters, occurred on March 22nd, 2014 in western Washington State. It caused significant property damage and killed over 40 people. As a result, a renewed interest has emerged for creating more accurate landslide susceptibility maps for this region. Research addressing landslide susceptibility within the north Puget Sound region of western Washington is lacking; therefore, this study develops a probabilistic GIS-based landslide susceptibility model for the north Puget Sound region. Multivariate logistic regression was utilized to create a landslide susceptibility map of Whatcom, Skagit, Snohomish, and King Counties. To predict probable areas of landslide occurrence, a landslide inventory map was prepared and fourteen topographic, geologic, environmental, and climatic predictor variables were considered. This research aims to assist in restructuring western Washington's landslide policies, and could serve as the first step in producing more accurate landslide susceptibility maps for the region.

scholarly journals Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

2021 ◽  
Author(s):  
Amol Sharma ◽  
Chander Prakash
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Road Construction ◽  
Himachal Pradesh ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
The Impact ◽  
Very High

Landslide susceptibility mapping has proved to be crucial tool for effective disaster management and planning strategies in mountainous regions. The present study is perused to investigate the changes in the landslide susceptibility of the Mandi district of Himachal Pradesh due to road construction. For this purpose, an inventory of 1723 landslides was generated from various sources. Out of these, 1199 (70%) landslides were taken in the training dataset to be used for modelling and prediction purposes, while 524 (30%) landslides were taken in the testing dataset to be used for validation purposes. Eleven landslide causative factors were selected from numerous hydrological, geological and topographical factors and were analyzed for landslide susceptibility mapping using three bivariate statistical models, namely; Frequency Ratio (FR), Certainty Factor (CF) and Shanon Entropy (SE). Two sets of LSM maps i.e. landslide susceptibility map natural (LSMN) and landslide susceptibility map road (LSMR), were generated using the above mentioned bivariate models and were divided into five landslide susceptibility classes namely; very low, low, medium, high and very high. These maps were analyzed for accuracy of prediction and validation using receiver operating characteristic (ROC) curves and area under curve (AUC) technique which indicated that all three bivariate statistical models performed satisfactorily with the SE model had the highest prediction and validation accuracy of 83-86%. Further analysis LSM maps confirmed that the percentage area in high and very high classes of land-slide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2013 ◽  
Vol 1 (2) ◽  
pp. 1001-1050 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Confidence Intervals ◽  
Landslide Susceptibility ◽  
Spatial Planning ◽  
Cross Validation ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
High Interest ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas which might be prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, the quality of the landslide susceptibility map and of the model applied to compute them is of high interest. In this study we focus on the analysis of the model performance by a repeated k-fold cross-validation with spatial and random subsampling. Furthermore, the focus is on the analysis of the implications of uncertainties expressed by confidence intervals of model predictions. The cross-validation performance assessments reflects the variability of performance estimates compared to single hold-out validation approaches that produce only a single estimate. The analysis of the confidence intervals shows that in 85% of the study area, the 95% confidence limits fall within the same susceptibility class. However, there are cases where confidence intervals overlap with all classes from the lowest to the highest class of susceptibility to landsliding. Locations whose confidence intervals intersect with more than one susceptibility class are of high interest because this uncertainty may affect spatial planning processes that are based on the susceptibility level.

scholarly journals Producing landslide susceptibility maps by applying expert knowledge in a GIS - based environment.

2016 ◽  
Vol 47 (3) ◽  
pp. 1539 ◽  
Author(s):  
P. Tsangaratos ◽  
D. Rozos
Keyword(s):  
Landslide Susceptibility ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Visual Basic ◽  
Susceptibility Map ◽  
Engineering Systems ◽  
Landslide Susceptibility Map ◽  
Related Factors ◽  
Susceptibility Maps ◽  
Analytic Hierarchical Process

In this paper two semi - quantative approaches, from the domain of Multi criteria decision analysis, such as Rock Engineering Systems (RES) and Analytic Hierarchical Process (AHP) are implemented for weighting and ranking landslide related factors in an objective manner. Through the use of GIS these approaches provide a highly accurate landslide susceptibility map. For this purpose and in order to automate the process, the Expert Knowledge for Landslide Assessment Tool (EKLATool) was developed as an extension tightly integrated in the ArcMap environment, using ArcObjects and Visual Basic script codes. The EKLATool was implemented in an area of Xanthi Prefecture, Greece, where a spatial database of landslide incidence was  available

scholarly journals Weights of Evidence Modeling for Landslide Susceptibility Mapping of Kabi-Gebro Locality, Gundomeskel area, Central Ethiopia

2020 ◽  
Author(s):  
Nega Getachew ◽  
Matebie Meten
Keyword(s):  
Landslide Susceptibility ◽  
Agricultural Land ◽  
Google Earth ◽  
Weights Of Evidence ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Data Set ◽  
Causative Factors ◽  
Central Highland ◽  
The Impact

Abstract Kabi-Gebro area is located within the Abay Basin at Dera District of North Shewa Zone near Gundomeskel town in the Central highland of Ethiopia and it is about 320 Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically characterized by weathered sedimentary and volcanic rocks. Currently, landslides are creating serious challenges in road construction, farming practices and affecting people in this area. Active landslides in this area damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map. To overcome the landslide problem in this area, landslide susceptibility map was prepared using GIS- based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslide locations were identified and classified randomly as training landslide (80%) and validation landslide (20%). The training landslide data set include nine landslide causative factors such as lithology, slope angle, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall inorder to prepare landslide susceptibility map in this study. The landslide susceptibility maps were prepared by adding the weights of contrast values of the nine causative factors using rater calculator in the spatial analyst tool of ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high landslide susceptiblity classes. This susceptibility map was validated using landslide density index and Area Under the Curve (AUC). The result from this validation showed a success rate and avalidaton rate accuracies of 82.4% and 83.4% respectively for this model. Finally, this study recommends application of appropriate mitigation or corrective measures in order to lessen the impact of landslide in the area.

scholarly journals Implementing Landslide Susceptibility Map at Watershed Scale of Lompobattang Mountain South Sulawesi, Indonesia

2018 ◽  
Vol 50 (2) ◽  
pp. 197
Author(s):  
Abdul Rachman Rasyid ◽  
Netra Prakash Bhandary ◽  
Ryuichi Yatabe
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Disaster Mitigation ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Watershed Scale ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Predictive Rate

This study attempts to predict future landslide occurrence at watershed scale and calculate the potency of landslide for each sub-watershed at Lompobatang Mountain. In order to produce landslide susceptibility map (LSM) using the statistical model on the watershed scale, we identified the landslide with landslide inventories that occurred in the past, and predict the prospective future landslide occurrence by correlating it with landslide causal factors. In this study, six parameters were used namely, distance from fault, slope, aspect, curvature, distance from river and land use. This research proposed the weight of evidence (WoE) model to produce a landslide susceptibility map. Success and predictive rate were also used to evaluate the accuracy by using Area under curve (AUC) of Receiver operating characteristic (ROC). The result is useful for land use planner and decision makers, in order to devise a strategy for disaster mitigation.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2014 ◽  
Vol 14 (1) ◽  
pp. 95-118 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Landslide Susceptibility ◽  
Additive Model ◽  
Susceptibility Map ◽  
Model Quality ◽  
Open Communication ◽  
Entire Study ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, several aspects of the quality of the landslide susceptibility model and the resulting classified map are of high interest. In this study of landslides in Lower Austria, we focus on the model form uncertainty to assess the quality of a flexible statistical modelling technique, the generalized additive model (GAM). The study area (15 850 km2) is divided into 16 modelling domains based on lithology classes. A model representing the entire study area is constructed by combining these models. The performances of the models are assessed using repeated k-fold cross-validation with spatial and random subsampling. This reflects the variability of performance estimates arising from sampling variation. Measures of spatial transferability and thematic consistency are applied to empirically assess model quality. We also analyse and visualize the implications of spatially varying prediction uncertainties regarding the susceptibility map classes by taking into account the confidence intervals of model predictions. The 95% confidence limits fall within the same susceptibility class in 85% of the study area. Overall, this study contributes to advancing open communication and assessment of model quality related to statistical landslide susceptibility models.

scholarly journals Analisis Peta Kerawanan Longsor di Kecamatan Tangse

2020 ◽  
Vol 5 (2) ◽  
pp. 310-316
Author(s):  
Nurmala Ramadhani Lubis ◽  
Hairul Basri ◽  
Muhammad Rusdi
Keyword(s):  
Landslide Susceptibility ◽  
Disaster Mitigation ◽  
Research Center ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Weighted Overlay ◽  
Tsunami Disaster ◽  
Landslides Susceptibility ◽  
Very High

Abstrak. Tanah longsor adalah bencana hidrometeorologi yang sering terjadi di Indonesia. Tujuan dari penelitian ini adalah untuk mengetahui daerah kerawanan longsor di Kecamatan Tangse Kabupaten Pidie. Metode penelitian ini menggunakan Weighted Overlay yang didalamnya melibatkan pembobotan dan pengharkatan. Hasil penelitian menunjukan bahwa kelas bahaya longsor tidak rawan 573,61 ha (0,73%), agak rawan 30.600,38 ha (38,98%), rawan 46.526,72 ha (59,27%) dan sangat rawan 805,40 ha (1,03%). Selain itu, peta distribusi kerawanan longsor  ini juga dibandingkan dengan metode yang lain yaitu Indeks Storie dan juga TDMRC (Pusat Studi Tsunami dan Mitigasi Bencna). Setelah dibandingkan didapatkan persamaan yaitu pada jumlah kelas bahaya longsor dan juga perbedaan pada luas masing-masing kelas bahaya longsor.Analysing Landslides Suscepetibility Map in Sub District TangseAbstract. Landslide is a hydrometeorologycal disaster that usually happens in Indonesia. The main goal of this research was to determine the level of landslides susceptibility in Tangse Sub District, Pidie District. This research employed Weighted Overlay which involve weighting and scoring of each parameters. The results indicated  a variety of susceptibility clasess, which were; low 573.61 ha (0.73%), moderate 30,600.38 ha (38.98%), high 46,526.72 ha (59.27%) and very high 805.40 ha (1.03%). Moreover, distribution of landslide susceptibility map is also compared to others method, namely Indeks Storie and TDMRC (Tsunami Disaster Mitigation Research Center).  After comparing is obtained the equation number of landslides susceptibility classes and differences of areas.

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