Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Abstract

This work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

Documentation of Landslide Occurrence and Significance in Mbonjo, Limbe Town, South-West Cameroon

“…Large landslides in this area are very rare unlike in other parts of the world (e.g. [45,46]). The Pearson correlation matrix for geometric parameters (Table 3) indicate a strong positive correlation between the slide volume and area (r = 0.97, between the length and area (r=0.88), and between the slide width and area (r=0.97).…”

Mass-Movement Causes and Landslide Susceptibility in River Valleys of Lowland Areas: A Case Study in the Central Radunia Valley, Northern Poland

“…The LR method was selected because of its greater reliability compared to other methods of susceptibility assessment; this aspect has been confirmed by many results of verification using ROC curves [66][67][68]. Furthermore, considering other studies on young morainic areas [20,69], it is expected that these models are suitable for achieving highly accurate landslide modelling at the regional level. This method is based on the following formula:…”

“…Landslide susceptibility maps were developed using logistic regression. This multivariate statistical method is a standard statistical model for LS assessment [20,[60][61][62][63][64][65]. The LR method was selected because of its greater reliability compared to other methods of susceptibility assessment; this aspect has been confirmed by many results of verification using ROC curves [66][67][68].…”

“…Approaches to and methods for assigning landslide susceptibility (LS) can be grouped into five broad categories, namely, (i) geomorphological mapping, (ii) analysis of landslide inventories, (iii) heuristic or index-based approaches, (iv) process-based methods, and (v) statistically based modelling methods [19]. However, the methods used in landslide susceptibility allow for the assessment of the role of anthropogenic factors to a limited extent [20], which is related to the difficulties caused by including factors that rapidly change in time and space in two-dimensional GIS analysis. Field surveys and stability analysis, as well as groundwater level and inclinometric monitoring, are carried out on individual landslides and, therefore, allow for the determination, to a greater extent, of the influence of anthropogenic actions on the activation and progression of landslides [21][22][23][24][25].…”

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

“…Due to the powerful data processing and mapping capabilities of geographic information system, it has been widely applied to draw landslide susceptibility mapping over the past decades [4]. Currently, the widely used models for predicting landslide susceptibility mainly include qualitative, quantitative, and artificial intelligence attribution analysis [5,6]. The use of quantitative attribution analysis model accounted for the main research model.…”