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Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment

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Abstract and Figures

Landslide susceptibility maps can be developed with artificial neural networks (ANNs). In order to train our ANNs, a digital elevation model (DEM) and a scar map of one previous event were used. Eleven attributes are generated, possibly containing redundant information. Our base model is formed by, essentially, one input (the DEM), eleven attributes, 30 neurons, and one output (susceptibility). Principal components (PCs) group information in the first projected variables, the last ones can be expendable. In the present paper, four groups of models were trained: one with eleven attributes generated from the DEM; one with 8 out of 11 attributes, in which 3 were eliminated by their high correlation with others; other, with the data projected over its PCs; and another, using 8 out of 11 PCs. The used number of neurons in hidden layer is 30, calibrated based on a complexity analysis that is an in-house developed method. The ANN models trained with the original data generated better statistical results than their counterparts trained with the PC transformed input. Keeping the original 11 attributes calculated provided the best metrics among all models, showing that eliminating attributes also eliminates information used by the model. Using 11 PC transformed attributes hindered trained. However, for the model with eight PCs, training was much faster than its counterpart with little accuracy loss. The metrics and maps achieved were considered acceptable, conveying the power of our model based on ANNs, which uses essentially one input (the DEM) for mapping areas susceptible to mass movements.
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/ Published online: 21January2020
https://doi.org/10.1007/s10661-019-7968-0
Attribute selection using correlations and principal
components for artificial neural networks employment
for landslide susceptibility assessment
Lu´
ısa Vieira Lucchese ·
Guilherme Garcia de Oliveira ·
Olavo Correa Pedrollo
Received: 12 May 2019 / Accepted: 11 November 2019
© Springer Nature Switzerland AG 2020
Abstract Landslide susceptibility maps can be devel-
oped with artificial neural networks (ANNs). In order
to train our ANNs, a digital elevation model (DEM)
and a scar map of one previous event were used.
Eleven attributes are generated, possibly containing
redundant information. Our base model is formed by,
essentially, one input (the DEM), eleven attributes,
30 neurons, and one output (susceptibility). Principal
components (PCs) group information in the first pro-
jected variables, the last ones can be expendable. In
the present paper, four groups of models were trained:
one with eleven attributes generated from the DEM;
one with 8 out of 11 attributes, in which 3 were elimi-
nated by their high correlation with others; other, with
the data projected over its PCs; and another, using 8
out of 11 PCs. The used number of neurons in hidden
layer is 30, calibrated based on a complexity analy-
sis that is an in-house developed method. The ANN
models trained with the original data generated bet-
ter statistical results than their counterparts trained
Lu´
ısa Vieira Lucchese ()·Olavo Correa Pedrollo
Instituto de Pesquisas Hidr´
aulicas, Universidade Federal do
Rio Grande do Sul, Av. Bento Gonc¸alves,9500, Porto Ale-
gre, Brazil
e-mail: luisa.lucchese@ufrgs.br
Guilherme Garcia de Oliveira
Departamento Interdisciplinar, Universidade Federal do Rio
Grande do Sul, Rodovia RS 030, 11700, km 92. Emboaba,
Tramanda´
ı, RS, 95590-000, Brazil
with the PC transformed input. Keeping the origi-
nal 11 attributes calculated provided the best metrics
among all models, showing that eliminating attributes
also eliminates information used by the model. Using
11 PC transformed attributes hindered trained. How-
ever, for the model with eight PCs, training was much
faster than its counterpart with little accuracy loss. The
metrics and maps achieved were considered accept-
able, conveying the power of our model based on
ANNs, which uses essentially one input (the DEM) for
mapping areas susceptible to mass movements.
Keywords Landslide ·Multilayer perceptron ·
Dimensionality reduction ·Susceptibility map
Introduction
Landslides are natural hazards that occur when a mass
of soil detaches from its place and slides down a
slope (Cruden 1991), possibly causing damage to lives
and properties. With worldwide population growth,
human occupation of hazardous areas has substan-
tially increased over the past decades, and the impact
of natural disasters has been largely magnified in both
industrialized and developing countries (Guzzetti et al.
1999). Between 1971 and 1974, nearly 600 people per
year were killed by landslides (Schuster and Fleming
1986). The fatality rate has increased to 4617 people
per year between 2014 and 2010, during which 32,322
Environmental Monitoring and Assessment (2020) 192: 129
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... Some of the attributes present high intercorrelations with each other. However, in Lucchese et al. (2020), it was shown that even attributes that were intercorrelated by a coefficient higher than 0.7 brought useful information to the model. ...
... The type of ANN used is a multilayer perceptron (MLP) with one hidden layer (Fig. 3), consisting of nh = 30 neurons. The number of neurons in the hidden layer is determined by using a novel approach (Lucchese et al., 2020). It consists in choosing the minimum number of neurons in the hidden layer for which the relationship between the input and the output variables is well represented. ...
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The present study is dealt with the preparation of landslide susceptibility map of Darjeeling Himalaya with the help of GIS tools and artificial neural network (ANN) model. Fifteen landslide causative factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were considered to produce the landslide susceptibility zonation map. To generate all these aforesaid causative factors map, topographical maps, geological map, soil map, satellite imageries, Google earth images and some other authorized maps were processed and constructed into a spatial data base using GIS and image processing techniques. The back-propagation method was applied to estimate factor’s weight and the landslide hazard indices were derived with the help of trained back-propagation weights. Then, the landslide susceptibility zonation map of Darjeeling Himalaya was made using GIS tool and classified into five, i.e. very low, low, moderate, high, and very low landslide susceptibility. To validate the prepared landslide susceptibility map, landslide inventory was used and accuracy result was obtained after processing ROC curve. The accuracy of the landslide susceptibility map was 81.5% which is desirable.
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Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilátero Ferrífero (642.13 km²). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement.