Abstract
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.
Highlights
Landslides are the most dangerous natural geologic processes that cause different types of damage and affect people, industries, and the environment, especially in times of dramatic climate change effects on the one hand, and urban sprawl and land consumption on the other
It provides a concise and precise description of the aim of this study. It is aimed at proposing a reliable Geographical Information System (GIS)-multicriteria decision analysis (MCDA) BW methodology for the landslide susceptibility mapping, which could serve as a useful tool for preventing and reducing the landslide hazard for spatial planners to create spatial policies and systems for landslide management
The various thematic data layers representing the landslide conditioning factors, such as the slope, the aspect, the elevation, lithology, the land use/cover, the distance to faults, the distance to rivers, the distance to roads, the topographic wetness index (TWI), the stream power index (SPI), the sediment transport index (STI), rainfall, the distance to urban areas, the soil type, and the normalized difference vegetation index (NDVI), were prepared. These factors fall under the three categories of the conditioning factors that make the area susceptible to movement without initiating a landslide; these factors are considered to be responsible for the occurrence of landslides in the regions for which pertinent data can be collected from the available resources and from the field
Summary
Landslides are the most dangerous natural geologic processes that cause different types of damage and affect people, industries, and the environment, especially in times of dramatic climate change effects on the one hand, and urban sprawl and land consumption on the other. Throughout the world, hundreds of thousands of houses and buildings have been destroyed and many people have been injured by and died due to landslides. The study of landslide susceptibility mapping is rapidly becoming the focus of major scientific research, engineering study, and practices throughout the world [1]. Many researchers have pursued work with the intention of predicting and preventing landslide hazards by using a wide variety of methods. A landslide susceptibility map represents the areas with the potential for landslides in the future by combining some of the critical factors that contributed to the occurrence of past landslides [2]
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