Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Ohlmacher, Gregory C.; Davis, John C.

doi:10.1016/s0013-7952(03)00069-3

Cited by 600 publications

(310 citation statements)

References 7 publications

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“…The independent variables (i.e. geological parameters) in this model are predictors of the dependent variable and can be measured on a nominal, ordinal, interval or ratio scale (Beguería & Lorente, 2002;Bledsoe & Watson, 2001;Lamelas, Marinoni, Hoppe, & de la Riva, 2008;Lei, Jiang, & Li, 2001;Ohlmacher & Davis, 2003). The LR model identifies the most significant factors controlling sinkhole collapse and assigns a weight to each independent variable in order to calculate a value of the dependent variable at the centroids of a 2 × 2 km grid cells.…”

Section: Methodsmentioning

confidence: 99%

Sinkhole susceptibility, Lazio Region, central Italy

Ciotoli

Loreto²,

Finoia

et al. 2015

Journal of Maps

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Lazio is a region particularly affected by catastrophic sinkhole collapse. For some areas sinkholes appear to be sporadic, whilst other areas are particularly affected by sinkholes. These events can cause considerable damage when they occur in an urban area or in areas with transport networks. For several years, the Lazio Region has conducted studies on sinkholes in order to define areas prone to this phenomenon. This study involved the use of cartographic sources, historical documentation, as well as field trips, in order to construct an inventory of sinkhole cases in the region, as well as to identify sinkhole prone areas (SPAs). The inventory allowed each sinkhole case to be analysed within the geological and hydrogeological setting, as well as the surrounding vegetation and degree of urbanisation, in which it occurred. Correlations were also performed with tectonic and structural trends, and with triggering factors, such as earthquakes. Some predisposing variables to sinkhole phenomena are then selected. The application of multivariate logistic regression and geospatial analyses allows the selection of the most significant factors, as well as their optimal weights used to construct a sinkhole susceptibility map of the Latium Region at the 1:250,000 scale. The identification of the susceptibility grade of SPAs will help in the management of these areas, based on the risk level.

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Section: Methodsmentioning

confidence: 99%

Sinkhole susceptibility, Lazio Region, central Italy

Ciotoli

Loreto²,

Finoia

et al. 2015

Journal of Maps

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“…In most of the similar natural hazard modelling's inventory data was used as a point format Lee et al 2012b;Rahmati et al 2016). The map was divided into a 70%-30% proportion for training and testing, respectively (Ohlmacher and Davis 2003). Training locations (137 points) were randomly selected for the production of the dependent data, consisting of 0 and 1 values, with 1 representing the existence, and 0 the absence of flooding.…”

Section: Flood Inventory Mapmentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

211

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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“…The method adopted in the literature to divide the susceptibility histogram into different categories is in many cases based on expert criteria (Dai and Lee, 2002;Ohlmacher and Davis, 2003;Van Den Eeckhaut et al, 2006) and does not take into account the real underlying data. It is necessary to explore data and obtain knowledge of their statistical distribution before applying any method of classification (Foote and Crum, 2014).…”

Section: Data Source and Landslide Susceptibility Mapmentioning

confidence: 99%

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

Baeza¹,

Lantada

Amorim

2016

Environ Earth Sci

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A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas. However, a classification system used for readability determines the final appearance of the map and may therefore influence the decision-making tasks adopted. The present paper addresses the spatial comparison and the accuracy assessment of some well-known classification methods applied to a susceptibility map that was based on a discriminant statistical model in an area in the Eastern Pyrenees. A number of statistical approaches (Spearman's correlation, Kappa index, Factorial and Cluster analyses and Landslide density index) for map comparison were performed to quantify the information provided by the usual image analysis.The results showed the reliability and consistency of the Kappa index against Spearman's correlation as accuracy measures to assess the spatial agreement between maps. Inferential tests between unweighted and linear weighted Kappa results showed that all the maps were more reliable in classifying areas of highest susceptibility and less reliable in classifying areas of low to moderate susceptibility. The spatial variability detected and quantified by Factorial and Cluster analyses showed that the maps classified by Quantile and Natural Break methods were the closest whereas those classified by Landslide Percentage and Equal Interval methods displayed the greatest differences. The difference image analysis showed that the five classified maps only matched 9% of the area. This area corresponded to the steeper slopes and the steeper watershed angle with forestless and sunny slopes at low altitudes. This means that the five maps coincide in identifying and classifying the most dangerous areas. The Equal Interval map overestimated the susceptibility of the study area and the Landslide Percentage map was considered to be a very optimistic model. The spatial pattern of the Quantile and Natural Break maps was very similar but the latter was more consistent and predicted potential landslides more efficiently and reliably in the study area.

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Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Cited by 600 publications

References 7 publications

Sinkhole susceptibility, Lazio Region, central Italy

Sinkhole susceptibility, Lazio Region, central Italy

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

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