An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Ba, Qianqian; Chen, Yumin; Deng, Shengyuan; Wu, Qianjiao; Yang, Jiaxin; Zhang, Jingyi

doi:10.3390/ijgi6010018

Cited by 62 publications

(35 citation statements)

References 51 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting value can be a positive or negative value, with the following class. If I <0, the probability of landslides is lower than average; If I = 0, the probability of a landslide is equal to the average; And if I> 0, the probability of landslides is higher than average (Ba et al, 2017). In other words, the higher the value of information, the higher the probability of a landslide occurrence.…”

Section: = / /mentioning

confidence: 98%

Visualized information value model result of landslide vulnerability in Purworejo

Sudaryatno¹,

Widayani²,

Wibowo³

et al. 2019

Sixth Geoinformation Science Symposium

View full text Add to dashboard Cite

Indonesia is one of the disaster-prone countries. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 65 landslides in Purworejo. Landslide is one of the most common disaster that occur in Indonesia. Landslide is caused by meteorological and geomorphological factors. Purworejo is one of the potential areas that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Landslide susceptibility modeling in Purworejo Regency was carried out using three different methods, namely Information Value Model (IVM), Information Value Model-Analytical Hierarchy Process (IVM-AHP) and Information Value Model-Gray Clustering (IVM-GC). The difference between this research and the landslide modeling research that has been done previously is in the geovisualization section. Research that has been done to model landslide vulnerability is visualized in the form of a two-dimensional map. This research develops visualization techniques with the aim of making reading easier, increasing traction, and adding height information and impressions to map readers.The visualization used is 3-dimensional mapping. This mapping is intended to make it easier to compare the map results of modeling that have been done before. The expected results of this study are accurate and reliable 3-dimensional visualization to study the advantages and disadvantages of each of the modeling methods used.

show abstract

Section: = / /mentioning

confidence: 98%

Visualized information value model result of landslide vulnerability in Purworejo

Sudaryatno¹,

Widayani²,

Wibowo³

et al. 2019

Sixth Geoinformation Science Symposium

View full text Add to dashboard Cite

show abstract

“…The spatial probability of a landslide vulnerability can be expressed as the probability of the spatial occurrence of slope failures with a set of geo-environmental conditions. However, due to the complex nature of landslides, producing a reliable spatial prediction of landslide susceptibility is not easy [3].…”

Section: Introductionmentioning

confidence: 99%

The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping

Gigović

Drobnjak

Pamučar

2019

IJGI

View full text Add to dashboard Cite

The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was prepared using the National Landslide Database, aerial photographs, and also by carrying out field surveys. A total of 1082 landslide locations were detected. This methodology considers the fifteen conditioning factors that are relevant to landslide susceptibility mapping: the elevation, slope, aspect, distance to the road network, distance to the river, distance to faults, lithology, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness Index (TWI), the Stream Power Index (SPI), the Sediment Transport Index (STI), annual rainfall, the distance to urban areas, and the land use/cover. The expert evaluation takes into account the nature and severity of the observed criteria, and it was tested by using two scenarios: the different aggregation methods of the BWM. The prediction performances of the generated maps were checked by the receiver operating characteristics (ROCs). The validation results confirmed that the areas under the ROC curve for the weighted linear combination (WLC) and the ordered weighted averaging (OWA) aggregation methods of the MCDA-BWM have a very high accuracy. The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes.

show abstract

“…For decades, landslide research has been a popular topic among the research communities. Many different methods have been utilized to reveal the link between landslide occurrence and various controlling factors for the purpose of landslide prediction, such as logistic regression [2][3][4], the information value method [5][6][7], frequency ratio [8], analytic hierarchy process [9,10], artificial neural network [11,12] and support vector machine [13]. Among the various methods, the logistic regression (LR) method has been widely used and reported to be applicable for landslide research [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Chen

Deng

et al. 2019

IJGI

Self Cite

View full text Add to dashboard Cite

Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran's I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.implies that these models failed to explain all of the spatial patterns inherent in the landslide data, and may have led to misspecification errors in the model.This study attempted to eliminate the negative influence of spatial autocorrelation on landslide susceptibility assessments by introducing eigenvector spatial filtering (ESF) into logistic regression. Spatial filtering, as discussed by Getis [21,22] and Griffith [23], was considered to be an effective approach for addressing spatial autocorrelation. The ESF method proposed by Griffith, utilizes eigenvectors generated from a given spatial connectivity matrix to account for redundant locational information resulting from spatial autocorrelation [24]. Several significant eigenvectors selected with a stepwise regression procedure are added to the linear regression model as independent variables to filter the spatial autocorrelation out from the regression residuals.This study was conducted in Wulong county and involved four main steps. First, the predisposing factors responsible for landslide occurrence were determined. Subsequently, landslide susceptibility models were developed. Then, evaluations were conducted and comparisons were made between the ESF-based logistic regression model and other models, including ordinary logistic regression and autologistic regression, to assess model performance. Finally, the models were employed to map landslide susceptibility throughout the study area. Study Area and Data Study AreaThe study area of Wulong county is located in the southeast section of the Chongqing Municipality (Figure 1) between longitudes (107 • 14 ) E and (108 • 5 ) E and latitudes (29 • 2 ) N and (29 • 40 ) N, and covers an a...

show abstract

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Cited by 62 publications

References 51 publications

Visualized information value model result of landslide vulnerability in Purworejo

Visualized information value model result of landslide vulnerability in Purworejo

The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Contact Info

Product

Resources

About