Abstract
The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.
Highlights
The rapid growth in data due to the development of information and communication technology (ICT) has spurred the demand for data mining, which is generally considered to be the most useful analytical tool in the analysis of large data sets
A detailed description of the data mining models used in this study is given below
The data mining models are suitable for observing the rapid changes in an urban area with its compactness and complex characteristics
Summary
The rapid growth in data due to the development of information and communication technology (ICT) has spurred the demand for data mining, which is generally considered to be the most useful analytical tool in the analysis of large data sets. Data mining is a process of finding useful information that is not exposed in a large amount of data, most recently known as “big data”. Rational and rapid decision-making is necessary to increase the utilization of large-scale databases. In this environment, it has become important to find meaningful new information that can support optimal decision-making. It has become important to find meaningful new information that can support optimal decision-making In this process, an efficient methodology for data mining that extracts known information from
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