Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network

Alkhasawneh, Mutasem Sh.; Ngah, Umi Kalthum; Tay, Lea Tien; Isa, Nor Ashidi Mat

doi:10.1007/s12665-013-3003-x

Cited by 33 publications

(15 citation statements)

References 18 publications

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“…From the validation of the landslide susceptibility maps, the RBF kernel produced AUC values, indicating the accuracy of the landslide susceptibility maps, and these were 81.36% for the PyeongChang area, and 77.49% for the Inje area (Figure 7). There were some differences in accuracy between the study areas, because the previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][29][30][31][32][33] showed that the spatial distribution is subject to change, according to the area and event. However, the accuracy was usually high enough, displaying figures of above 80%.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, recent case studies have frequently applied soft computing technology to the assessment of landslide hazards. When creating soft computing models, artificial neural networks [2][3][4][5][6], neuro-fuzzy logic [2,[7][8][9], decision trees [10][11][12][13][14][15], and support vector machines (SVMs) [10,[15][16][17][18][19], have been applied in order to analyze landslide landslide susceptibility. Among the many soft computing models, SVMs were applied in the present study.…”

Section: Introductionmentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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Abstract:In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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“…Understanding earth surface processes relies on modern digital terrain representations and depends strongly on the quality of the topographic data (Tarolli et al 2009). Digital elevation model (DEM) was used as a basic data source to extract topographic data (Alkhasawneh et al 2014).…”

Section: Data Fusion Of Ground Measurements and Satellite Observationmentioning

confidence: 99%

A fundamental theorem of Earth’s surface modelling

et al. 2016

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Ground observation is able to obtain highly accurate data with high temporal resolution at observation points, but these observation points are too sparse to satisfy some application requirements. Satellite remote sensing and system models can frequently supply spatially continuous information about the Earth's surface, which is impossible from ground-based investigations, but remote sensing description and system model simulation are not able to directly obtain process parameters. The most effective method for Earth surface modelling entails the integration of satellite observations or system models with ground observations. However, the full integration was ignored in most of the methods. For finding a solution for this problem, we suggest an alternative method, high accuracy surface modeling (HASM), which takes global approximate information (e.g., remote sensing images or model simulation results) as its driving field and local accurate information (e.g., ground observation data and/or sampling data) as its optimum control constraints. HASM completes its operation when its output satisfies the iteration stopping criterion which is determined by application requirement for accuracy. A Fundamental Theorem of Earth Surface Modelling (FTESM) is abstracted on the basis of applying HASM to simulating surfaces of elevation, soil properties, changes of ecosystem services, and driving forces of the changes on multi-scales for about 20 years. FTESM is described as ''an Earth's surface system or a component surface of the Earth's surface environment can be simulated with HASM when its spatial resolution is fine enough, which is uniquely defined by both extrinsic and intrinsic invariants of the surface''. From FTESM, seven corollaries have been deduced, corresponding to interpolation, upsaling, downscaling, data fusion and data assimilation respectively.

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“…Interpolation methods are used to estimate the unknown elevation value of grid cells from known points according to different geometry or physical mechanisms (Yue 2011). Classical spatial interpolation methods such as triangulated irregular network with linear interpolation (TIN), spline, inverse distance weighted (IDW) and kriging could satisfy many practical applications in environmental modelling, whereas applications such as hydrological modelling, and landslide hazard mapping, etc., are demanding DEM surfaces with higher accuracy (Alkhasawneh et al 2014;Blöschl and Sivapalan 1995). To further improve the accuracy of interpolated DEM surface and satisfy these accuracy-demanding applications, high-accuracy surface modelling (HASM) method was developed with the foundation of the first and second fundamental coefficients (Yue et al 2007.…”

Section: Introductionmentioning

confidence: 99%

Speeding up the high-accuracy surface modelling method with GPU

et al. 2015

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In order to find a solution for accurate, topographic data-demanding applications, such as catchment hydrologic modeling and assessments of anthropic activities impact on environmental systems, high-accuracy surface modeling (HASM) method is developed. Although it can produce a digital elevation model (DEM) surface of higher accuracy than classical methods, e.g. inverse distance weighted, spline and kriging, HASM requires numerous iterations to solve large linear systems, which impede its applications in high-resolution and large-scale surface interpolation. This paper aims to demonstrate the utilization of graphics' processing units (GPUs) device to accelerate HASM in constructing large-scale and highresolution DEM surfaces. We parallelized the linear system algorithm for solving HASM with Compute Unified Device Architecture, a parallel programming model developed by NVIDIA. We designed a memory-saving strategy to enable the HASM algorithm to run on GPUs. The speedup ratio of GPU-based algorithm was tested and compared with CPUbased algorithm through simulations of both ideal Gaussian synthetic surface and real topographic surface in the loess plateau of Gansu province. The GPU-parallelized algorithm can attain an over 109 speedup ratio with the CPUbased algorithm as a reference. The speedup ratio increases with the scale and resolution of the dataset. The memory management strategy efficiently reduces the memory usage by more than eight times the grid cell number. Implementing HASM in the GPUs device enables modeling large-scale and high-resolution surfaces in a reasonable time period and implies the potential benefits from the use of GPUs as massive, parallel co-processors for arithmeticintensive data-processing applications.

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Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network

Cited by 33 publications

References 18 publications

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A fundamental theorem of Earth’s surface modelling

Speeding up the high-accuracy surface modelling method with GPU

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