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

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

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Landslide Susceptibility Based on Extreme Rainfall-Induced Landslide Inventories and the Following Landslide Evolution

Water ◽

10.3390/w11122609 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2609 ◽

Cited By ~ 3

Author(s):

Chunhung Wu

Keyword(s):

Landslide Susceptibility ◽

Extreme Rainfall ◽

Susceptibility Map ◽

Typhoon Morakot ◽

Landslide Susceptibility Map ◽

Related Factors ◽

Extreme Rainfall Events ◽

River Watershed ◽

Correct Ratio ◽

Landslide Evolution

Landslide susceptibility assessment is crucial for mitigating and preventing landslide disasters. Most landslide susceptibility studies have focused on creating landslide susceptibility models for specific rainfall or earthquake events, but landslide susceptibility in the years after specific events are also valuable for further discussion, especially after extreme rainfall events. This research provides a new method to draw an annual landslide susceptibility map in the 5 years after Typhoon Morakot (2009) in the Chishan River watershed in Taiwan. This research establishes four landslide susceptibility models by using four methods and 12 landslide-related factors and selects the model with the optimum performance. This research analyzes landslide evolution in the 5 years after Typhoon Morakot and estimates the average landslide area different ratio (LAD) in upstream, midstream, and downstream of the Chishan River watershed. We combine landslide susceptibility with the model with the highest performance and average annual LAD to draw an annual landslide susceptibility map, and its mean correct ratio ranges from 62.5% to 73.8%.

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Landslide Susceptibility in Majalengka Regency, West Java Province

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/884/1/012053 ◽

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%.

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LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY

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

10.5194/isprs-archives-xliii-b3-2020-1229-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1229-1236 ◽

Cited By ~ 1

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.

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