Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Li, Y.; Chen, G.; Tang, Chuan; Zhou, Guangpeng; Zheng, Lu

doi:10.5194/nhess-12-2719-2012

Cited by 57 publications

(32 citation statements)

References 31 publications

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“…Numerous landslide predictive factors have been used in producing spatial hazard maps in previous studies [8,9,14,19,20,30]. According to [31], these factors can be grouped into two types: (1) controlling factors that contribute to landslides potential, such as slope, lithology, topography, geology and hydrology; and (2) triggering factors, such as rainfall, earthquakes and human activities (e.g., excavation at the foot of slope, mining, etc.).…”

Section: Landslide Predictive Factormentioning

confidence: 99%

“…Typical examples of quantitative methods are frequency ratio (FR) [14,15], information value model [16], weight of evidence (WoE) [17] and logistic regression (LR). In addition, some machine learning methods, such as support vector machines (SVM) [18,19], artificial neural network (ANN) [20] and backpropagation artificial neural networks (BPANN), have become increasingly popular in recent years. Many comparative studies of these methods have been conducted, such as [11,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

et al. 2016

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Abstract:The development of landslide susceptibility maps is of great importance due to rapid urbanization. The purpose of this study is to present a method to integrate the subjective weight with objective weight for regional landslide susceptibility mapping on the geographical information system (GIS) platform. The analytical hierarchy process (AHP), which is subjective, was employed to weight predictive factors' contribution to landslide occurrence. The frequency ratio (FR) method, which is objective, was used to derive subclasses' frequency ratio with respect to landslides that indicate the relative importance of a subclass within each predictive factor. A case study was carried out at Tsushima Island, Japan, using a historical inventory of 534 landslides and seven predictive factors: elevation, slope, aspect, terrain roughness index (TRI), lithology, land cover and mean annual precipitation (MAP). The landslide susceptibility index (LSI) was calculated using the weighted linear combination of factors' weights and subclasses' weights. The study area was classified into five susceptibility zones according to the LSI. In addition, the produced susceptibility map was compared with maps generated using the conventional FR and AHP method and validated using the relative landslide index (RLI). The validation result showed that the proposed method performed better than the conventional application of the FR method and AHP method. The obtained landslide susceptibility maps could serve as a scientific basis for urban planning and landslide hazard management.

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Section: Landslide Predictive Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

et al. 2016

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“…Previous studies have applied probabilistic models including an AHP: Analytic Hierarchy Process, Arti cial Neural Network, Dempster-Shapfer theory of evidence, fuzzy logic and Monte Carlo methods [1][2][3][4][5][6][7][8][9][10][11] among statistical models, the logistic regression model has also been applied to landslide susceptibility mapping [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. More sophisticated assessments have involved weight of evidence approaches and frequency ratio [25][26][27][28][29][30][31][32][33][34][35][36][37][38] Research on rainfall probability calculation has primarily been limited to improving the rainfall probability predictions accuracy and to studies targeting water resources [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Lee

Jeon

et al. 2016

Open Geosciences

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Abstract:The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with arti cial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identi ed based on interpretations of optical remote sensing data (Aerial photographs) followed by eld surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using arti cial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the arti cial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the arti cial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the arti cial neural network model, 'slope' yielded the highest weight value (1.330), and 'aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the ndings, we were able to derive a more e ective method for analyzing landslide susceptibility.

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“…An ANN subjectively assigns weights to different conditioning factors with negligible human interference. The weights indicate the relative importance of the factors (Li et al, 2012). ANNs are influential tools that are applicable in classification, prediction, and pattern recognition applications (Kia et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A New Framework for Geospatial Site Selection Using Artificial Neural Networks as Decision Rules: A Case Study on Landfill Sites

Abujayyab

Ahamad

Yahya

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015.As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decisionmaking problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.

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Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Cited by 57 publications

References 31 publications

GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

A New Framework for Geospatial Site Selection Using Artificial Neural Networks as Decision Rules: A Case Study on Landfill Sites

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