A Data Mining Approach To Landslide Prediction
Author(s)
F. T. Souza & N. F. F. Ebecken
Abstract
The study of landslides is a very difficult task due to the huge space-temporal
variety of the involved parameters. The study of Rio de Janeiro’s city landslide
problem has been performed by a Data Mining approach. The dataset related to
the landslides registers between 1998 and 2001, including meteorological and
soil parameters, are the basis of this work. The cumulative rain patterns related to
the landslides depend on the missing data replacement, which was analyzed by
several methods, including Clustering and Statistical Analysis. The rain spatial
analysis selected the rain gauges to be input on Neural Networks (NN), which
were used to replace the missing rain values. The landslide volume variable also
presents missing values and their completion has been performed by a K-nearest
neighbor method. After data preparation, some models (using NN, Interesting
Association Rules and Classification Rules) were built to predict the accidents
and rainfall, improving the existing alert system.
Keywords: data mining, geographical information systems (GIS), landslides.
1 Introduction
The decontrolled urbanization process in the large cities imposes alterations in
the environmental nature equilibrium. Many areas near the slopes are occupied
by a vast part of population and these occupations promote the deforesting, soil
vegetable cover destroying, garbage accumulation, etc. The new atypical
conditions impose a new setup of the soil hillsides, which turned it susceptible to
the occurrences of landslides during the rainfall.
Studies Menezes et al [1] have been shown that Rio de Janeiro’s city has
physical characteristics (geographical position and topography) and
atmospherically patterns favorable to development of meteorological
phenomenon that causes the rainfall. The summer months are rainy and more
than 60% of the landslides generally occur during these months.
Keywords
data mining, geographical information systems (GIS), landslides.
Related Book
Other papers in this volume
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