Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1088390

A comparative study of random forests and multiple linear regression in the prediction of landslide velocity


Krkač, Martin; Bernat Gazibara, Sanja; Arbanas, Željko; Sečanj, Marin; Mihalić Arbanas, Snježana
A comparative study of random forests and multiple linear regression in the prediction of landslide velocity // Landslides, 17 (2020), 11; 2515-2531 doi:10.1007/s10346-020-01476-6 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1088390 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
A comparative study of random forests and multiple linear regression in the prediction of landslide velocity

Autori
Krkač, Martin ; Bernat Gazibara, Sanja ; Arbanas, Željko ; Sečanj, Marin ; Mihalić Arbanas, Snježana

Izvornik
Landslides (1612-510X) 17 (2020), 11; 2515-2531

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Landslide monitoring, Groundwater level prediction, Landslide movement prediction, Random forests, Multiple linear regression

Sažetak
The monitoring of landslides has a practical application for the prevention of hazards, especially in the case of large deep-seated landslides. Monitoring data are necessary to understand the relationships between movement and triggers, to predict movement, and to establish an early warning system. This paper compares two phenomenological models for the prediction of the movement of the Kostanjek landslide, the largest landslide in the Republic of Croatia. The prediction models are based on a 4-year monitoring data series of landslide movement, groundwater level, and precipitation. The presented models for landslide movement prediction are divided into the model for the prediction of groundwater level from precipitation data and the model for the prediction of landslide velocity from groundwater level data. The statistical techniques used for prediction are multiple linear regression and random forests. For the prediction of groundwater level, 75 variables calculated from precipitation and evapotranspiration data were used, while for the prediction of landslide movement, 10 variables calculated from groundwater level data were used. The prediction results were mutually compared by k-fold cross-validation. The root mean square error analyses of k-fold cross-validation showed that the results obtained from random forests are just slightly better than those from multiple linear regression, in both, the groundwater level and the landslide velocity models, proofing that multiple linear regression has a potential for prediction of landslide movement.

Izvorni jezik
Engleski

Znanstvena područja
Geologija, Rudarstvo, nafta i geološko inženjerstvo, Interdisciplinarne tehničke znanosti

Napomena
Glavni autori: Martin Krkač i Željko Arbanas



POVEZANOST RADA


Ustanove:
Građevinski fakultet, Rijeka,
Rudarsko-geološko-naftni fakultet, Zagreb

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Krkač, Martin; Bernat Gazibara, Sanja; Arbanas, Željko; Sečanj, Marin; Mihalić Arbanas, Snježana
A comparative study of random forests and multiple linear regression in the prediction of landslide velocity // Landslides, 17 (2020), 11; 2515-2531 doi:10.1007/s10346-020-01476-6 (međunarodna recenzija, članak, znanstveni)
Krkač, M., Bernat Gazibara, S., Arbanas, Ž., Sečanj, M. & Mihalić Arbanas, S. (2020) A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides, 17 (11), 2515-2531 doi:10.1007/s10346-020-01476-6.
@article{article, author = {Krka\v{c}, Martin and Bernat Gazibara, Sanja and Arbanas, \v{Z}eljko and Se\v{c}anj, Marin and Mihali\'{c} Arbanas, Snje\v{z}ana}, year = {2020}, pages = {2515-2531}, DOI = {10.1007/s10346-020-01476-6}, keywords = {Landslide monitoring, Groundwater level prediction, Landslide movement prediction, Random forests, Multiple linear regression}, journal = {Landslides}, doi = {10.1007/s10346-020-01476-6}, volume = {17}, number = {11}, issn = {1612-510X}, title = {A comparative study of random forests and multiple linear regression in the prediction of landslide velocity}, keyword = {Landslide monitoring, Groundwater level prediction, Landslide movement prediction, Random forests, Multiple linear regression} }
@article{article, author = {Krka\v{c}, Martin and Bernat Gazibara, Sanja and Arbanas, \v{Z}eljko and Se\v{c}anj, Marin and Mihali\'{c} Arbanas, Snje\v{z}ana}, year = {2020}, pages = {2515-2531}, DOI = {10.1007/s10346-020-01476-6}, keywords = {Landslide monitoring, Groundwater level prediction, Landslide movement prediction, Random forests, Multiple linear regression}, journal = {Landslides}, doi = {10.1007/s10346-020-01476-6}, volume = {17}, number = {11}, issn = {1612-510X}, title = {A comparative study of random forests and multiple linear regression in the prediction of landslide velocity}, keyword = {Landslide monitoring, Groundwater level prediction, Landslide movement prediction, Random forests, Multiple linear regression} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font