Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

Wang, Liangjie; Guo, Min; Sawada, Kazuhide; Lin, Jie; Zhang, Jinchi

doi:10.1016/j.catena.2015.08.007

Cited by 140 publications

(88 citation statements)

References 42 publications

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“…Several methods of sampling are used for susceptibility. Many studies confirm that the best sampling method is when the ratio of pixels with desertification to pixels without desertification is equal to 1 [38,98,99]. Thus, we used the same number of samples for both the presence and absence of desertification.…”

Section: Input Database For Logistic Regression Analysismentioning

confidence: 99%

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

2017

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Abstract:The main goal of this work was to identify the areas that are most susceptible to desertification in a part of the Algerian steppe, and to quantitatively assess the key factors that contribute to this desertification. In total, 139 desertified zones were mapped using field surveys and photo-interpretation. We selected 16 spectral and geomorphic predictive factors, which a priori play a significant role in desertification. They were mainly derived from Landsat 8 imagery and Shuttle Radar Topographic Mission digital elevation model (SRTM DEM). Some factors, such as the topographic position index (TPI) and curvature, were used for the first time in this kind of study. For this purpose, we adapted the logistic regression algorithm for desertification susceptibility mapping, which has been widely used for landslide susceptibility mapping. The logistic model was evaluated using the area under the receiver operating characteristic (ROC) curve. The model accuracy was 87.8%. We estimated the model uncertainties using a bootstrap method. Our analysis suggests that the predictive model is robust and stable. Our results indicate that land cover factors, including normalized difference vegetation index (NDVI) and rangeland classes, play a major role in determining desertification occurrence, while geomorphological factors have a limited impact. The predictive map shows that 44.57% of the area is classified as highly to very highly susceptible to desertification. The developed approach can be used to assess desertification in areas with similar characteristics and to guide possible actions to combat desertification.

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Section: Input Database For Logistic Regression Analysismentioning

confidence: 99%

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

2017

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“…Medium Elevations: Fields at medium elevations are assessed as more susceptible to landslides because of the ground cover that forms due to accumulating of materials, weathering, and erosion coming from higher fields (Dai and Lee, 2001;Dai and Lee, 2002;Ercanoğlu, 2003;Lan et al, 2004;Görüm, 2006;Dağ, 2007;Yüksel, 2007;Gorsevski and Jankowski, 2008;Hasekioğulları, 2011;Kavazoğlu et al, 2012;Çellek, 2013;Elkadiri et al, 2014;Wang et al, 2015;Dragicevic et al, 2015;Avcı, 2016 a, b, c;Opiso, et al, 2016;Abedini et al, 2017;Pradhan and Kim, 2017). Yüksel (2007) determined in a study that medium-elevation areas are more susceptible to landslides compared with lower or higher sections.…”

Section: Elevation Classesmentioning

confidence: 99%

“…0-50, 50-100, 100-200, 200-300, >300 yok Sadr et. al 20140-100, 100-200, 200-300, 300-425 100 Shrestha et, al., 2017<1281, 1281-1755, 1755-2254, 2254 -9.01, 9.01-18.02, 18.02-45.07, 45.07-90.14, 90.14-135,21, 135,21-198,31, 198,31-297,45, 297,46-458, 73, 468,73-748, 18, 748,18-2298,62 yok Wang et al, 201520-850, 850-1000, 1000-1150, 1150-1300, 1300-1560yok Wang and Li, 2017. <900, 900-1300, 1300-1700, 1700-2100, 2100-2500 ve> 2500 400 <150, 150-200, 200-250, 250-300, 300-350, 350-400, 400-450, 450-500, 500-550, >550 50 Wu et.…”

Section: Class Interval Rangesmentioning

confidence: 99%

Morphological parameters causing landslides: a case study of elevation

Çellek¹

2019

Bulletin of the Mineral Research and Exploration

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The history of landslide susceptibility maps goes back about 50 years. Danger and risk maps later followed these maps. Inventory maps provide the source of all these. There are different parameters selected specially for each field in the literature as well as parameters selected because they are easy to produce and obtain data. One of the parameters that are easy to access and prepare and are acquired from the secondary derivatives of topographical data is the parameter of elevation.This study tried to research the effect of elevation on landslides by reviewing the literature in detail. The used class ranges and elevation values were reviewed and applied to the map sections selected from Turkey. By analyzing the results, the goal was to determine at which elevation ranges landslides occurred.The study tried to investigate the effect of the parameter of elevation using data from the literature. It works to compare the elevation values for map sections selected to compare with the literature. The study comprises two stages. The first step tried to acquire statistical data by researching the data from the literature. The data were investigated in the second stage. For this purpose, close to 1,500 studies prepared between 1967 and 2019 were reviewed. According to the literature, the parameter of was used in analyses because it is easy to produce and is morphologically effective.

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“…There is limited availability of literature of MARS in transformation of coordinates studies but many studies have successfully applied MARS for solving different problems in engineering. Some of the areas of applications include estimating energy demand [39], slope stability analysis [31,40,41,42], landslide susceptibility mapping [43], water pollution prediction [44], earthquake modelling [45], studying ecological variables [46], region spatio-temporal mapping [47], modelling of the ionosphere [48], geothermal prospect [49] and so on.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

Peprah¹,

Mensah²,

Akresi³

2017

JGG

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In Ghana's local Geodetic Reference Network, the standard forward transformation equation has played a major role in coordinate transformation between World Geodetic System 1984 (WGS84) and local geodetic datum. Thus, it is an initial step in forward conversion of geodetic coordinates (ϕ , λ, h) to Cartesian coordinates (X, Y, Z) in transformation from global to local datum and vice versa. Several studies in the recent decades have been conducted on converting Cartesian coordinates to geodetic coordinates (reverse procedure) through the utilisation of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on the existing literature covered pertaining to this present study, it was found that the existing knowledge do not fully adhere to the issue of evaluating alternative techniques in the case of the forward conversion. Hence, the aim of this present study was to explore the coordinate conversion performance of the Multivariate Adaptive Regression Splines ( ). The statistical findings revealed that the MARS and MLR offered satisfactory prediction of Cartesian coordinates. However, the MLR compared to MARS showed better stability and more accurate prediction results. From the results of this present study, the main conclusion drawn is that, MLR provides a promising alternative in the forward conversion of geodetic coordinates into Cartesian coordinates. Therefore, the capability of MLR as a powerful tool for solving majority of function approximation problems in mathematical geodesy has been demonstrated in this present study.

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Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

Cited by 140 publications

References 42 publications

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

Morphological parameters causing landslides: a case study of elevation

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

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