GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria)

Bourenane, Hamid; Bouhadad, Youcef; Guettouche, Mohamed Saïd; Braham, Massinissa

doi:10.1007/s10064-014-0616-6

Cited by 89 publications

(36 citation statements)

References 60 publications

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“…The information value (IV) method, originally proposed by Yin and Yan (1988) and slightly modified by van Westen (1993), is a statistical analysis method developed from information theory (Xu et al 2013). This model has been widely used in landslide susceptibility analysis by various researchers (van Westen 1993;Bui et al 2011;Bhandary et al 2013;Xu et al 2013;Bourenane et al 2015). In this model, the IV for each parameter class is defined as the natural logarithm of the landslide density in a class divided by the landslide density of the entire map (Equation 1) (van Westen 1993; Bui et al 2011).…”

Section: Information Value (Iv) Methodsmentioning

confidence: 99%

“…Quantitative methods are indirect mapping techniques, based on mathematical expressions of the correlation between causal variables and landslides (Wang et al 2014). Over the years, both types of methods have been used in conjunction with geographic information systems (GIS) to assess landslide susceptibility (Dahal et al 2008;Regmi et al 2010;Mohammady et al 2012;Kayastha et al 2013;Bourenane et al 2015;Sujatha 2017;Sujatha and Sridhar 2017). There are many statistical models used in landslide hazard analysis, including the frequency ratio method (Mohammady et al 2012;Kayastha 2015;Bourenane et al 2016;Chen et al 2017a), statistical index method (Bui et al 2011;Pourghasemi et al 2013a;Razavizadeh et al 2017), weights of evidence method (Dahal et al 2008;Razavizadeh et al 2017), certainty factor method (Kanungo et al 2011;Sujatha et al 2012), and logistic regression method (Ayalew and Yamagishi 2005;Lee and Pradhan 2007;Yilmaz 2009;Chen et al 2017c).…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Wang

Guo

et al. 2019

Geomatics, Natural Hazards and Risk

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Landslide susceptibility mapping is essential in delineating landslide prone areas in mountainous regions. The primary purpose of this study is to evaluate landslide susceptibility mapping using three methods, information value (IV), weights-of-evidence (WofE), and certainty factor (CF) in southeastern Gansu province, China. Firstly, the landslide in the study area was located mainly using aerial-photo interpretation and field surveys. A total of 529 landslides were randomly divided using a ratio of 70/30 for training and validation of these methods. Eight landslide conditioning factors including slope angle, altitude, NDVI, distance to faults, distance to roads, distance to rivers, rainfall, and lithology, were considered. Landslide susceptibility maps were produced for each of the three methods. Susceptibility maps were verified and compared using the area under the curve (AUC) method. The success rate curve demonstrated that the AUC for the IV, WofE, and CF methods was 88.02, 87.56, and 89.04%, respectively, and the prediction rate curve showed that the AUC was 86.24, 86.34, and 87.44%, respectively. Furthermore, results showed that CF method had the highest accuracy in comparison with other methods. Generally, the three methods showed reasonable accuracy in landslide susceptibility mapping. Results of this study can serve as guidelines to managers and policy makers regarding the prevention and mitigation of landslide hazards.

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Section: Information Value (Iv) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Wang

Guo

et al. 2019

Geomatics, Natural Hazards and Risk

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“…The procedure for this method is fast and reasonably simple, which makes it suitable for natural hazard modelling (Aghdam et al 2016). Wi weights can be described as the natural logarithm of the flood existence in each class of a conditioning factor divided by the total flood density in the study area (Bourenane et al 2015). The following equation is used to calculate Wi weights for each factor (Chen et al 2016):…”

Section: Flood Susceptibility Map Produced By Statistical Index (Wi)mentioning

confidence: 99%

Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods

Tehrany

Kumar

Jebur

et al. 2018

Geomatics, Natural Hazards and Risk

162

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“…In Algeria, landslide susceptibility mapping presents a novel approach for prevention of the risks linked to the landslides. So far, few attempt at landslide susceptibility were applied in Algeria (Hadji et al, 2013;Djerbal and Melbouci, 2013;Hadji et al, 2014;Bourenane et al, 2015;Achour et al, 2017;Dahoua et al, 2017;Mahdadi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using information value and frequency ratio for the Arzew sector (North-Western of Algeria)

Roukh

Abdelmansour

2018

Bulletin of the Mineral Research and Exploration

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Geological hazards present one of the most important constraints for the development of the Arzew sector (Oran province), North Western of Algeria. Landslides are considered us one of the most common phenomena in the study area and especially in the hilly area. For minimizing and reducing the consequences of this problem, it is necessary to carry out preliminary studies on the cartography of the different zones exposed to the slope instability phenomena. The main objective of this study is to perform the landslide susceptibility mapping by statistical models and GIS techniques for the Arzew area. To achieve this goal, an analytical approach was carried out. Firstly, a landslide inventory map was prepared using previous inventory maps, satellite images, aerial photos and field surveys. Secondly seven conditioning factors such as slope degree, aspect, lithology, land use, distance to the streams, distance to the road and altitude were exploited to assess landslide susceptibility. Thirdly, the weight value for each class of the conditioning factors was determined using Frequency Ratio (FR) and Information Value (IV) models based in GIS functionalities. Consequently, Landslide Susceptibility Maps (LSMs) were produced by the classification process of the global Landslide Susceptibility Indexes (LSIs) into five classes. Finally, for experiment verification, the LSMs obtained with the FR and IV models were confirmed comparing LSMs with landslide inventory map using both the Receiver Operating Characteristics (ROC) and the Seed Cell Area Index (SCAI) models. The area under curve (AUC) results, demonstrate that the IV method more performance (89.03%) for LSM than FR method (85.57%). Furthermore, the validation results using SCAI also confirmed that the IV model was more accurate than FR model. The models employed in this study are capable to resolve the issue of the landslide susceptibility of the study area. The produced susceptibility maps can be used for future land use planning and can be considered as a powerful tool to resolve the spatial distribution of the risk associated to landslides.

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GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria)

Cited by 89 publications

References 60 publications

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods

Landslide susceptibility mapping using information value and frequency ratio for the Arzew sector (North-Western of Algeria)

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