Citation

Lucchese LV, de Oliveira GG, Pedrollo OC. Environ. Monit. Assess. 2020; 192(2): e129.

Affiliation

Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves,9500, Porto Alegre, Brazil.

Copyright

(Copyright © 2020, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10661-019-7968-0

PMID

31965339

Abstract

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.

Language: en

Keywords

Dimensionality reduction; Landslide; Multilayer perceptron; Susceptibility map